
Over the past year, I've had countless conversations with businesses trying to navigate the complex world of AI agent pricing. As the founder of [Agentman](https://agentman.ai), I've seen firsthand how pricing confusion can become a major barrier to AI adoption, especially for smaller businesses. Let's explore the four core pricing models that have emerged in the market and their real-world implications.

## The Four Core Pricing Models

### 1. Per-Execution (Run-Based) Pricing
At [Agentman](https://agentman.ai), we are pioneering this approach specifically for the SMB market. The concept is simple: you pay one fixed price for each completed task your agent performs, regardless of complexity or duration. Think of it like hiring a virtual employee—you're paying for outcomes, not inputs.

This model emerged from hundreds of conversations with business owners who wanted predictable costs without becoming AI experts. Whether your agent handles a quick product query or manages a complex multi-step return process, the price remains constant. All technical costs—API calls, model usage, infrastructure—are included, ensuring no surprises.

### 2. Outcome-Based Pricing
Sierra.ai has embraced a results-oriented approach, charging only when agents achieve specific business outcomes. This model is ideal for sales-focused use cases, such as generating qualified leads or closing sales.

However, defining success can be tricky for customer support or operational tasks. Questions like "What constitutes a successful outcome?" or "How should partial successes be billed?" complicate calculations. For instance, does resolving a delayed order count if the customer remains dissatisfied? These challenges often lead to disputes and complex billing.

### 3. Per-Conversation Pricing
Salesforce's Agentforce illustrates both the appeal and limitations of this model. At $2 per conversation, it works well for straightforward support interactions but struggles with complex enterprise scenarios. For sales agents, Salesforce switches to per-lead pricing, and for custom agents, they use a credit-based system.

Beyond these limitations, Salesforce's platform dependencies significantly increase costs. SMBs must also purchase Salesforce software licenses ($100s per user monthly), API call allowances, and Data Cloud access. For many, these additional costs make this model impractical.

### 4. Usage-Based Pricing
This model charges based on computational resources used (e.g., API calls, tokens processed, or compute time). While developers may appreciate this approach, it creates unnecessary complexity for most business users who prefer to focus on solving problems rather than monitoring token usage.

Platforms like Langbase and Wordware use this model but often combine it with hybrid options to appeal to a broader audience.

## The Rise of Hybrid Models

Many platforms now mix and match pricing approaches. For instance:

- **Salesforce** combines $2 per conversation for prebuilt agents with an entirely different "AI Credits" system for custom agents, involving calculations based on message counts, API calls, and Data Cloud usage.
- **Sierra.ai** uses outcome-based pricing for some use cases and switches to consumption-based pricing for others, depending on complexity.

While hybrid models offer flexibility, they also increase complexity, making it harder for businesses to predict costs. Developers often spend excessive time optimizing for costs rather than improving user experience.

## The Hidden Costs Nobody Talks About

In my experience, platform and implementation costs often catch businesses off guard. Here's what to expect:

- **Enterprise Platforms**: Platforms like Salesforce's Agentforce and Sierra.ai typically require $50,000 to $200,000 in professional services fees and 3-6 months of implementation time.
- **Ongoing Costs**: Include licensing fees, training, and integration work.

## Different Businesses, Different Needs

### Enterprises:
- Usage-based pricing can work well for enterprises with technical teams capable of managing complex integrations and optimizing usage.
- Large companies often negotiate hybrid models tailored to their specific needs.

### SMBs:
- Simplicity and predictability are key. At [Agentman](https://agentman.ai), our per-execution model allows SMBs to focus on growth without worrying about complicated pricing calculations.

## Real-World Cost Comparison

Here's an example for handling 10,000 customer interactions monthly:

![Comparison of AI Agent pricing models showing costs for different pricing approaches including Outcome-Based, Per-Conversation, Usage-Based, and Per-Execution models](/images/blog/comparing-agent-ai-pricing-models-calculations.jpeg)
*Figure: Cost comparison of different AI agent pricing models for 10,000 monthly interactions*

Note: Factor in implementation and maintenance costs, which vary based on agent customization.

## Looking Ahead

The AI agent pricing landscape is evolving rapidly. Key trends to watch:

- **Industry-Specific Pricing Models**: Tailored to vertical markets.
- **Value-Based Pricing**: Emphasizing measurable business outcomes.
- **Simplified SMB Solutions**: Easier adoption for smaller businesses.
- **Transparent Total Cost of Ownership**: Better clarity on true costs.

## Making Your Choice

Based on my experience, here are six steps to guide your decision:

- **Evaluate Current Costs**: Assess your costs without AI agents and potential savings or revenue gains.
- **Define ROI Goals**: Prioritize use cases with clear ROI potential.
- **Assess Technical Capabilities**: Understand your team's ability to manage complexity.
- **Estimate Interaction Volume**: Determine scalability requirements.
- **Consider Implementation Costs**: Be mindful of setup and ongoing management expenses.
- **Plan for Future Scaling**: Choose a model that accommodates growth.

Remember, the cheapest option isn't always the most cost-effective. Simplicity and predictability can save money by making costs easier to manage.

## Join the Conversation

What's been your experience with AI agent pricing? Share your challenges and successes in the comments below. Let's learn from each other and continue the conversation!


The Complete Guide to AI Agent Pricing Models in 2025


Imagine having a personal assistant who is always there, quietly working in the background and only stepping in when something truly needs your attention. That's precisely what Guardian Agents are—AI assistants (called ambient agents in AI circles) that operate proactively without requiring constant interaction. While the term "ambient agents" is becoming popular among the AI community, we prefer "Guardian" agents because that's precisely what they do—they protect your time and attention, taking care of what matters while letting you focus on what's important.

At [Agentman](https://agentman.ai), we're thrilled to announce that Guardian Agents are coming to our platform in the coming weeks. Soon, you'll be able to buy, build, and sell Guardian Agents that seamlessly work with over 7,000 apps, transforming how businesses automate their operations.

## Why Guardian Agents Are Different

Traditional AI chat agents that [Agentman](https://agentman.ai) recently launched are like having to tap someone on the shoulder every time you need something. You must open a chat window, type out your request, and await a response. It's reactive and requires constant initiation from you.

Guardian Agents flip this model on its head. They work proactively in your digital environment and take action when needed, only involving you in important decisions or when they need guidance. It's like having a highly competent assistant who knows when to handle things independently and when to ask for your input.

## Real-World Examples of Guardian Agents in Action

### 1. The Always-On Business Analyzer

Meet Sarah, a business owner using multiple SaaS tools. Her Guardian Business Agent:
- Monitors data across Salesforce, HubSpot, and QuickBooks
- Identifies revenue opportunities and customer churn risks
- Alerts Sarah only when there's an unusual pattern or significant opportunity
- Draft action plans based on cross-platform insights
- Automatically updates dashboards and reports across all systems

Instead of Sarah having to check various platforms or manually analyze data, her agent delivers important insights directly to her while managing routine tasks autonomously.

### 2. The Intelligent Workflow Coordinator

Consider TechSavvy Solutions, a growing company using various productivity tools. Their Guardian Workflow Agent:
- Continuously analyzes tasks across Asana, Slack, and Gmail
- Automatically prioritizes and routes work to the right team members
- Flags potential bottlenecks before they become critical
- Creates and updates project timelines based on real-time progress
- It only interrupts managers when it detects unusual patterns or urgent situations

The team no longer needs to coordinate manually across platforms; their agent manages everything, highlighting only what needs human attention.

### 3. The Proactive Marketing Manager

Think about Marcus, who runs digital marketing campaigns. His Guardian Marketing Agent:
- Monitors performance across Google Ads, Meta, and LinkedIn
- Automatically adjusts budgets based on real-time performance
- Alerts him only when there's a significant performance change
- Drafts new ad variations based on successful patterns
- Identifies cross-channel optimization opportunities

Marcus no longer needs to juggle multiple advertising platforms – his agent handles routine optimizations automatically while bringing important decisions to his attention.

### 4. The Personal Ring Whisperer

This is my favorite. Meet the Oura Guardian Agent, a fascinating example of how personal data can drive meaningful insights:
- Analyzes your Oura Ring data alongside your calendar and daily activities
- Identifies which meetings cause the most stress through heart rate analysis
- Correlates your peak performance times with your daily patterns
- Connects your sleep quality to your work output and social media success
- Provides amusing but insightful observations about your habits

This agent transforms raw health data into actionable lifestyle insights, only interrupting when it spots important patterns or opportunities for improvement.

## Coming Soon to Agentman

We're excited to announce that we will make it supereasy to build and deploy Guardian Agents in the coming weeks. This new capability will revolutionize how you buy, build, and sell agents that work with over 7,000 apps through our platform.

Whether you're looking to:
- Buy pre-built Guardian Agents for immediate deployment
- Build your own Guardian Agents without coding
- Sell your Guardian Agents to others in our marketplace

Our platform will make it simple to create agents that work proactively across all your favorite tools and platforms.

## Transform Any Agent into a Guardian One

Soon you'll be able to:
- Convert existing agents into Guardian Agents
- Set up custom monitoring parameters
- Define intelligent interaction patterns
- Create multi-app workflows
- Build sophisticated automation with minimal oversight

## The Future of AI Assistance

Guardian Agents are the next evolution in Agents. Rather than being tools that wait for your command, these agents become true partners that work alongside you, understanding when to act independently and when to seek your input.

This shift from reactive to proactive Agent assistance means:
- Less time spent on routine tasks
- Fewer interruptions for minor decisions
- More focus on strategic work
- Better scalability of your operations
- Improved response times to critical situations

Stay tuned for more updates as we prepare to bring Guardian Agents to [Agentman](https://agentman.ai). Want to be among the first to try it out? Join our waitlist to get early access when we launch.

How do you see Guardian Agents transforming your personal life or business operations?


Introducing Guardian Agents (Ambient Agents): The Future of Agent Assistance


## Introduction

The landscape of artificial intelligence is rapidly evolving, and at its forefront are [AI agents](/blog/what-are-ai-agents) - tools that can autonomously perform tasks, drive conversations, and enhance business processes. We've seen AI agents progress from cumbersome, code-heavy frameworks to the rise of [no-code platforms](/blog/no-code-ai-development) and, more recently, to Agent-First Apps. This evolution makes AI more accessible than ever, empowering businesses of all sizes to transform their operations.

## Overview of AI Agent Evolution

AI agents have undergone a remarkable transformation. In the early days, building agents required extensive coding and deep technical knowledge, making them accessible to only a few. For example, frameworks like LangChain and LLamaIndex require proficiency and a solid understanding of Python to build agents. However, the rise of no-code platforms has shifted the paradigm. Businesses no longer need specialized programming skills to harness the power of AI. Today, anyone with an idea can leverage no-code tools like Salesforce's Agentforce and Google's Vertex AI Agent Builder to create intelligent assistants.

Beyond accessibility, we're moving towards pre-built agents and, ultimately, Agent-First Apps - where intelligent agents are at the core of a business's operational model. This evolution is the next frontier, offering companies new ways to improve customer experiences, automate workflows, and streamline operations.

## Importance of These Trends

The trends we're about to explore are fundamentally reshaping business operations, customer experiences, and employee workflows. They're also making AI accessible to a broader audience, giving businesses the tools to innovate faster and more effectively. From simplifying AI development to tailoring solutions for specific industries, these trends democratize AI and open new possibilities for organizations worldwide.

![AI Agent Evolution Timeline showing the progression from code-first frameworks to no-code platforms](/images/blog/7-Trends-Shaping-the-Future-of-AI-Agents-Future.jpg)
*The evolution of AI agents from complex frameworks to accessible no-code platforms*

### Trend 1: The Shift from Code-First Towards No-Code Agent Builders

The first significant trend is the shift towards no-code platforms, simplifying AI development. The recent introduction and popularization of these no-code agent builders, such as Salesforce's Agentforce and Google's Vertex AI Agent Builder, educate customers that no-code agent builders can build complex agents, allowing even non-technical users to build sophisticated AI agents.

The shift from building agents in code to no-code agent builders. This democratization of AI is being driven by the increasing demand for attaining ROI on AI. Companies want to innovate faster, solve customer problems more efficiently, and integrate AI into everyday workflows.

### Trend 2: Adopting Pre-Built Agents to Jumpstart AI Projects

With the rise of pre-built agents, businesses no longer need to start from scratch - even with no-code tools. Many companies prefer to deploy ready-made support, sales, and customer service agents. This trend particularly appeals to organizations lacking technical skills, as pre-built agents offer faster time-to-market, reduced complexity, and cost-effective solutions.

Pre-built agents need to be customizable to be widely adopted and used by many customers. If no-code agent builders are available, even better, as they allow businesses to start with a pre-built agent, test it, customize it, iterate, and deploy - continuously improving the agent to meet their unique needs - without involving AI Engineers.

### Trend 3: Verticalization of AI Agents

Another trend shaping the future of AI is the verticalization of AI agents. Startups are increasingly building agents tailored to specific industries - such as healthcare, legal, financial services, and software development - addressing unique workflows and challenges within those sectors.

Companies like Abridge (focused on healthcare) and Harvey (targeting legal services) are prime examples of this specialization. Vertical agents offer tailored solutions, handling industry-specific nuances more effectively and improving efficiency and outcomes for their users.

### Trend 4: The Rise of Agent-First Apps in SaaS

The evolution of Software as a Service (SaaS) is shifting towards Agent-First Apps, where AI agents are becoming the primary interface to the apps, enabling employees to accomplish a lot quickly. Rather than relying on manual processes, businesses are increasingly looking to AI agents to handle workflows, customer interactions, and internal processes.

Platforms like Zapier and Retool are moving towards agent-driven automation, signaling a broader shift toward AI-led workflows. This trend transforms SaaS from traditional user-driven models into intelligent systems capable of autonomously understanding and executing complex tasks.

### Trend 5: Large and Well-Funded Startups Focus on Enterprises

Large and well-funded startups primarily focus on enterprises or enterprise use cases, leaving small and medium-sized businesses (SMBs) underserved. The current generation of no-code Agent Builders often requires integration with enterprise software such as Salesforce or cloud providers like Google Cloud, which can demand thousands of dollars in implementation services.

For example, Sierra.ai charges $100,000 as a starting price for its projects, which typically require a consulting engagement. Additionally, companies like Accenture, IBM, and even hyperscalers are charging upwards of $50,000 to do AI transformation projects or implement Generative AI MVPs.

### Trend 6: Leveraging API Platforms to Build Cross-App Conversational AI Agents

Workato's expansion into conversational AI agents (Workato Genies) signals a shift towards agentic orchestration, where AI agents are not built on a single app ecosystem but are being built across multiple app ecosystems using cross-platform APIs to handle complex, cross-functional workflows. These AI agents engage users through conversational interfaces like Slack and Microsoft Teams, making it easier for employees to work across multiple SaaS clouds such as ServiceNow, Salesforce, Workday, and more.

### Trend 7: Generative UI - Bringing Rich Interactions to Chat-Based Agents

Last but not least, generative UI is an important step in transforming chat conversations. Users no longer need to sacrifice rich interactivity when engaging with agents through chat. Instead, they can experience app-like functionality directly in a conversation.

Salesforce's Generative Canvas for Salespeople is an example of this transformation. Thanks to generative AI-powered UX integrated into Salesforce CRM, the Generative Canvas provides dynamically generated experiences that utilize the entire screen. These experiences include text, tables, charts, trends, and other rich visual Lightning components to represent key metrics.

## Conclusion: The Future of AI Agents - A Unified Platform for All

From no-code agent builders to pre-built solutions and agent-first apps, these trends shape the future of AI development and adoption. Businesses of all sizes can leverage these technologies to improve efficiency, reduce costs, and offer personalized experiences to their customers.

As these trends converge, AI agents are becoming integral to business operations, automating complex workflows, improving customer interactions, and enabling organizations to innovate faster. The future of AI is about bringing the power of agents to everyone - making intelligent automation the new normal for businesses worldwide.


From No-Code Builders to Agent-First Apps - 7 Trends Shaping AI Agents


Imagine a future where Generative AI doesn't just respond to queries but proactively solves complex problems across every facet of business. This isn't science fiction; it's the rapidly approaching reality of Generative AI agents. These agents are poised to revolutionize companies' operations and inspire a new wave of innovation, from streamlining supply chains to optimizing product development and transforming customer interactions.

Having spent over a year building Generative AI applications and agents, I've seen firsthand how these technologies can profoundly reshape business processes. AI's potential is immense, from support agents that handle customer queries with unprecedented efficiency to autonomous agents that drive business operations and decision-making. These agents are not merely enhancing existing processes but enabling new ways of working.

For example, picture an agent that doesn't just schedule meetings but understands the context of your work, suggests the most impactful attendees, prepares briefing documents, and even proposes agenda items based on recent company developments. Or consider an agent in manufacturing that doesn't just monitor production lines but predicts maintenance needs, optimizes resource allocation in real-time, and collaborates with design teams to suggest product improvements based on production data.

This AI-driven transformation is creating a need for two pivotal roles: the AI Agent Product Manager and the AI Agent Engineer. These specialists are not just the architects and builders of our AI-augmented future, but integral parts of a collaborative team, working at the intersection of business strategy and cutting-edge technology.

![Primary responsibilities of AI Agent Product Manager and AI Agent Engineer](/images/blog/Agent-PM-and-Agent-Engineer.png)

## Introducing the New Roles

The AI Agent Product Manager is a visionary who identifies opportunities for agents to create value, designs their capabilities, and ensures they align with business goals and user needs. They act as translators between the world of business and AI possibilities, orchestrating AI innovation.

The AI Agent Engineer, on the other hand, is the technical wizard who brings these agents to life. They design robust architectures, create sophisticated prompts, and ensure the agents are grounded in company data and processes by seamlessly integrating them with various systems and data sources.

Since we are still in the early stages of this technology cycle, these professionals are usually found at specialized AI consultancies or companies developing agent-building products like Salesforce. This allows them to bring best practices and industry innovations to each new project.

## The AI Agent Product Manager: Orchestrating AI Innovation

As an Agent Product Manager, you may work on different use cases, such as a sales agent one month and an HR agent the next. Let's dive into what your role might look like:

As an Agent Product Manager, lets say you're tasked with developing an agent for a multinational manufacturing firm. Your first step is to lead a series of workshops with executives from various departments — operations, design, sales, and customer service. You're not just looking for incremental improvements; you're hunting for transformative opportunities, and you do this by fostering collaboration and understanding across the organization.

Through these discussions, you identify a game-changing possibility: an agent that can bridge customer feedback, product design, and manufacturing processes. This Agent would analyze customer reviews and support tickets, identify trending issues or desired features, and automatically generate design modification proposals. It would then simulate these changes' impact on manufacturing processes and costs.

As a product manager for agents, one of your primary responsibilities will be to map out the agents' journeys. It involves defining each step from the initial interaction to the final outcome, ensuring that everything aligns with the business goals. You will need to identify the key interactions the Agent will have, understand the context of these interactions, and determine the objectives each journey should achieve. You will also need to consider critical questions such as: How will the Agent prioritize customer feedback? How can it effectively present design suggestions to the engineering team? And what ethical considerations must be addressed when AI influences product decisions?

You'll work closely with stakeholders to define success metrics. For example, you may decide that the Agent should aim to reduce the time from identifying a product issue to implementing a fix by 50% while also increasing customer satisfaction scores.

As the project progresses, you ensure the agent delivers real business value. You might review both simulated and actual conversations between the AI and design teams, tweaking the Agent's communication style to better resonate with engineers. Or you could pore over data on how the Agent's suggestions have impacted product quality and customer satisfaction, looking for ways to improve its performance further.

Throughout this process, you're not just thinking about the Agent's current capabilities but its future potential. How could this Agent evolve to react to customer feedback and predict future market trends? Could it someday participate in brainstorming sessions with the product team, offering data-driven insights to fuel innovation?

Your Agent Product Manager role puts you at the forefront of business transformation. You're not just implementing a new tool; you're reshaping how entire organizations think, innovate, and operate in the age of AI.

## The AI Agent Engineer: Crafting Intelligent and Reliable Systems

Now, let's switch gears and step into the role of an Agent Engineer on this same project:

Your challenge is to create an agent that can understand customer feedback, translate it into actionable design insights, and interface with manufacturing systems. This is no small feat — it requires a deep understanding of large language models, sophisticated prompt engineering, and robust system integration.

You begin by selecting an appropriate large language model as the foundation for your Agent. However, your real work lies in designing a comprehensive agent architecture that can reliably perform across many conversation journeys.

As an Agent Engineer, one of your primary focuses is creating and refining the Agent's prompt structure. You craft intricate prompts that effectively guide the model's behavior, ensuring it consistently provides relevant and accurate responses across various scenarios. This could involve developing a hierarchical prompt system that can handle everything from supervising multiple agents to navigating various journeys.

You will spend significant time evaluating agent behavior and output, refining the prompts and flows, and publishing a new version. You may even design and implement a rigorous testing framework that simulates thousands of potential conversation trajectories. Your goal is to ensure that the Agent's response is deterministic and aligns with the desired outcome for any given input.

For instance, you might create a suite of test cases that cover various types of customer feedback, from simple product issues to complex feature requests. You then methodically work through these cases, analyzing the Agent's responses and iterating on the prompt structure and decision-making logic to improve performance.

When you encounter edge cases where the Agent's behavior is inconsistent or suboptimal, you don't simply tweak the prompt. Instead, you dive deep into the Agent's decision-making process, adjusting the underlying logic and prompt structure to address these issues systematically.

Integration remains a crucial part of your role. You're designing APIs that allow the Agent to pull data from customer support databases, access product design files, and input data into manufacturing planning systems. But beyond just connecting systems, you're focused on ensuring the Agent can make intelligent decisions based on this integrated data.

Ethics and safety continue to be critical concerns. You implement robust safeguards and oversight mechanisms to ensure the AI doesn't suggest design changes that could compromise product safety. You also build features for explainability, so the AI can always show its reasoning for any suggestion, which is crucial for building trust with the engineers and designers working with the Agent.

Your role as an Agent Engineer involves creating a functional AI system and crafting an intelligent agent that can reliably and consistently drive innovation and efficiency across the entire product development and manufacturing process. This complex challenge puts you at the forefront of AI technology, shaping the future of how businesses operate in the AI age.

## Ethical Considerations and the Power of Collaboration

As agents become more integral to businesses, Agent Product Manager and Agent Engineer roles will only grow in importance. These roles are not just about technical prowess or strategic insight — they demand a deep commitment to ethical considerations. As these agents influence significant business decisions, the professionals behind them must ensure that these systems are transparent, fair, and aligned with broader societal values.

The success of this agent relies heavily on the seamless collaboration between the Product Manager and Engineer. Together, you'll iterate on the Agent's capabilities, troubleshoot issues, and push the boundaries of what's possible.

## Comparing the Roles: Agent Product Manager vs. Agent Engineer

Here's a summary comparison table to emphasize the differences between the Agent Product Manager and the Agent Engineer:

![Comparing Agent Product Manager and Agent Engineer](/images/blog/Comparing-Agent-Product-Manager-and-Agent-Engineer.webp)

### Key Takeaways:

- **Agent Product Manager**: This position focuses on the strategic and business aspects of agents, ensuring they deliver value and align with company goals.
- **Agent Engineer**: This position concentrates on technical implementation, ensuring agents function reliably and integrate seamlessly with existing systems.

## The Future is Yours: Rise to the Challenge

As AI expands its influence, Agent Product Manager and Agent Engineer roles will be at the forefront of this technological revolution. Whether you're defining the strategy for an AI-driven business transformation or designing the intricate systems that power intelligent agents, you'll be shaping the future of business.

These roles require a unique blend of skills: strategic thinking, technical expertise, creativity, and a deep understanding of business and AI. They offer the chance to work on cutting-edge technology while driving tangible business impact.

So, future Agent Product Managers and Engineers, are you ready to rise to the challenge? The AI-augmented future is waiting for your expertise and vision. Whether you're drawn to the strategic aspects of product management or the technical intricacies of agent engineering, there's a place for you in this exciting new field. The question is not if AI will transform business, but how — and you could be the one to decide.


The Rise of the AI Agent Product Manager and AI Agent Engineer


When ChatGPT went public over a year ago, it gave the world unfettered access to the most advanced AI models. We were able to realize firsthand what AI can do for us. We began imagining how to use it to unleash our creativity and boost productivity.

Large language models (LLMs) have dismantled all the barriers to entry, empowering businesses of all sizes to unlock the transformative power of AI, regardless of budget or technical expertise. Many of you have built compelling LLM applications.

But with great power comes great complexity — choosing the right path to build and deploy your LLM application can feel like navigating a maze. Based on my experience guiding LLM implementations, I present a strategic framework to help you choose the right path. This framework categorizes LLM applications into three distinct approaches, each tailored to your specific needs, skillset, and time-to-market goals.

![Strategic framework for Building LLM applications in your business](/images/blog/Strategic-framework-for-Building-Generative-AI-in-Your-Business.png)

## Off-the-Shelf Gen AI

One way to simplify the process is by adopting a "plug-and-play" approach. You can achieve this by utilizing pre-trained and hosted models like OpenAI or Palm 2. These models offer developer-friendly APIs that allow you to build advanced applications with minimal technical expertise. Frameworks such as LangChain and LLamaIndex make this even more easier.

By understanding the complementary strengths of the following three fundamental techniques — prompt engineering, functions & agents, and RAG — you can unlock LLMs' full potential and build truly transformative applications.

### The Secret Sauce: Prompt Engineering

For this approach to be successful, it is essential to provide the right instructions. That's where prompt engineering comes in. Your prompts must be clear, with detailed commands telling the model what you want it to do and should not do. Remember to include the context and data to generate the correct response and personalize it by telling it what style and tone.

If you feel the responses are generic or verbose, employ few-shot learning to teach the LLM by example.

### Supercharge Your Model with Superpowers: Functions and Agents

If you need to go beyond text generation, look into employing functions and agents. Imagine your LLM App ordering pizza based on your preferences — that's the magic of functions and agents. Functions are tiny extensions, enabling your model to interact with APIs, databases, and other systems.

You can employ agents if you build applications beyond a simple question and answer or a simple task. Agents can understand the entire context, break down multi-step objectives into smaller steps, and adapt their responses across multiple conversation turns. Imagine an agent seamlessly navigating a complex customer service interaction, analyzing sentiment, reasoning through the issue, and making informed decisions across multiple exchanges.

Combining the flexibility of functions and agents with the power of prompt engineering will unlock a broader range of possibilities for your LLM applications.

### Knowledge is Power: Boosting Accuracy with Retrieval-Augmented Generation (RAG)

Prompt engineering relies on crafting instructions for the model, but it can't guarantee factual accuracy or real-world grounding. RAG solves this by retrieving relevant information from a knowledge base before generating a response. Imagine asking your LLM app for historically accurate creative content or a chatbot confidently answering policy questions based on internal knowledge. That's the magic of RAG.

You can combat hallucinations by verifying information and preventing fabricated details. Plus, you can ask the LLMs to explain their responses by citing your sources. Finally, RAG excels at understanding context, leading to nuanced and relevant responses in complex situations.

By leveraging one or more of these three techniques, you can develop complex LLM applications capable of summarizing support conversations, searching through thousands of documents, and creating task-oriented chatbots.

The best part is that you don't need to hire AI engineers for this; full-stack engineers would suffice. And, since you are utilizing proprietary models, you don't need to worry about the complexities of hosting these models.

You can launch your applications swiftly and with minimal expenses.

## Customized Gen AI Models

While the Off-the-shelf approach offers plug-and-play convenience, some tasks demand a custom approach. Enter Customized Gen AI models, where you take pre-trained models and fine-tune them like a race car for optimal performance.

If you want the model to understand your domain, data, and brand voice without including that with every prompt, fine-tune the model. For example, a travel company specializing in travel recommendations can fine-tune a pre-trained model to understand its unique data and brand voice. This model could generate personalized itineraries that consider their customers' past travel preferences, current weather conditions, and ongoing events — making them feel like VIPs.

### Level up your control:

With fine-tuned models, you can write highly converting product descriptions for your eCommerce site, generate medical reports on your specific clinical data, or understand the sentiment of customer reviews.

Though fine-tuning a model is slightly more expensive and time-consuming than the off-the-shelf approach, it is still cheaper than training a model from scratch, as you are only tweaking a smaller subset of the model's parameters to suit your needs.

This path also does not require AI expertise. Full-stack engineers with data science skills can effectively fine-tune and integrate models into your infrastructure.

If you are using proprietary models, you can save time and money on DevOps.

## Train Your Own Models on Proprietary Knowledge

There comes a point when you need a Gen AI solution tailor-made to your unique requirements — something that off-the-shelf or even fine-tuned models can't fully address. That's where training your own models on proprietary knowledge enters the picture.

Imagine a hospital developing a custom AI model trained on their vast patient data. This model could analyze medical scans and predict disease risk with unprecedented accuracy, potentially saving lives and revolutionizing healthcare.

### Mastering Your Domain:

Training custom models are compelling when dealing with niche domains or sensitive data requiring more specificity. Whether it's creating predictive models for financial markets or diagnostic tools for rare diseases, this path allows you to leverage your proprietary datasets to develop models that can provide insights and accuracy that generic models cannot match.

### Innovating on Your Terms:

When you train your own models, you innovate on your terms. You choose the data, training methods, and performance metrics. This approach is for forward-thinking businesses building unique AI capabilities.

### Navigating the Challenges:

Training machine learning models from scratch is challenging and resource-intensive. With careful planning, you can gain complete control over the AI's capabilities, and the potential for competitive advantage and innovation is vast.

### Big investments:

This route demands more data, expertise, and time. You'll need a team of ML engineers, data scientists, and engineers for model building, training, and lifecycle management. It's a significant investment, but the ROI can be substantial for high-stakes industries.

## Choosing the right path

We've explored three paths to building your LLM applications, each with unique advantages and challenges. To help you navigate this golden triangle of Time, Cost, and Control, I summarized the three paths in this handy comparison table:

![Comparing different LLM App Development Paths](/images/blog/Comparing-different-LLM-App-Development-Paths.png)

## Conclusion

So, which path will you take? Whether you're a budding entrepreneur or a seasoned leader, there's a generative AI approach that can empower your business. Start exploring today, unleash your creativity, and let AI be the wind beneath your wings.


A Strategic Framework for Building LLM Applications


Hello, fellow innovators! If you've ever chatted with ChatGPT, you'll know that it has a fantastic knack for crafting intelligent responses and facts. But did you know that GPT, the underlying technology behind ChatGPT, is a bonafide reasoning machine? Yes, that's right! We can supercharge our creations by merging our human strengths with GPT's capabilities. Ready to learn how? Let's dive right in!

## GPT: A Reasoning engine

At its core, GPT is a reasoning machine. Sure, it's excellent at retrieving facts, answering queries, and crafting fiction. But its true prowess lies in its ability to reason, draw conclusions, and formulate logical responses. However, despite this impressive capability, it lacks what makes us human: our real-world experiences, intuitive leaps, and creative problem-solving abilities.

## Is GPT Good At All Reasoning Types?

Let's examine this question by breaking it down by reasoning type.

Deductive reasoning is like solving a puzzle. Given a set of premises, we draw a logical conclusion. Business analysts do this when analyzing reports, and GPT is no stranger to it.

Inductive reasoning involves making broad generalizations based on specific instances. Spot a trend from particular data points? That's induction, another area where GPT shines.

However, other forms of reasoning are outside of GPT's scope but well within ours:

- **Emotional reasoning** calls for understanding and processing complex emotions, which we humans excel at, given our empathic nature, and crucial in most companies with customers.
- **Ethical reasoning** requires grappling with moral nuances, where our deep understanding of business ethics comes into play.
- **Abductive reasoning** involves making educated guesses, often filling information gaps. It's not GPT's strong suit, but it is our strength.
- Sometimes we know things without being able to explain why. This is called **intuitive reasoning**, which we are better at than AI.

## How About Knowledge? GPT Seems to Know Everything

To answer the above question, it will be helpful to look at the different classes of knowledge:

- **Factual knowledge** is knowing the latest stock price.
- **Conceptual knowledge** is understanding the concept of 'market capitalization.'
- **Procedural knowledge** is knowing how to draft a business plan.

GPT does an admirable job with these types of knowledge, thanks to its training on a vast text corpus. But the other types of knowledge fall outside its purview:

- **Experiential knowledge** comes from firsthand business experiences, something unique to each company.
- **Tacit knowledge**: We know but can't easily articulate this, like recognizing a potentially profitable business opportunity.
- **Personal knowledge** involves our business strategies, preferences, and decisions.
- **Propositional knowledge** entails understanding complex theoretical concepts, often industry-specific.

This is where we come in with our unique business experiences, unspoken understanding, personal strategies, and profound grasp of complex concepts.

## What if we combine Human Expertise with GPT's capabilities?

Imagine combining GPT's powerful reasoning capabilities and diverse knowledge types with our unique strengths. That combination will be formidable. We could supercharge our applications, leading to advancements previously thought to be in the realm of sci-fi.

If we enrich GPT with the business context, augment it with domain-specific knowledge, and employ external agents, we could solve complex tasks like analyzing proprietary marketing data, building a responsive customer service assistant, and even tailoring an education platform to a user's unique learning style. The possibilities are virtually limitless.

## Harnessing the Power of Human-AI Collaboration

By leveraging our human cognition and ChatGPT's reasoning abilities, we stand at the forefront of an exciting era of AI development. The future of AI is about more than lines of code or state-of-the-art algorithms; it's about the synergistic combination of human intelligence and AI capabilities. Let's use our knowledge to fuel innovation and shape the future of AI-powered applications!

## Real-world GPT applications

Let's take this collaboration concept for a spin and see how it could play out in the real world. I've imagined some scenarios where human insight and GPT's power combine to do amazing things in business applications. Buckle up.

### Customer Service Bot

Here's a thought — your business builds a customer service bot using GPT. It deals with all the standard queries, complaints, and requests using GPT's strength in deductive reasoning — that logical stuff that helps with straightforward questions. It's even got inductive reasoning skills, which are great for spotting patterns or trends in customer behavior. But if a customer's issue gets tricky or sensitive, it steps back and tags in a human agent. That's your cue to bring empathy and judgment to resolve the issue.

### Market Analysis Sidekick

Now, let's talk market trends. You could use GPT to create a tool that does the data processing heavy lifting and identifies patterns using that handy inductive reasoning. But who will make the final strategic decisions based on these trends? That's you, with your experiential knowledge and intuitive reasoning, understanding the broader context and potential impacts.

### Product Development Collaborator

During a new product's brainstorming and initial design phases, GPT's deductive reasoning can generate loads of ideas based on existing data. It's like a creative think tank! But the final decision is all yours. That's when your experiential and tacit knowledge about past successes and target audience preferences come into play.

### Legal Document Decoder

If you're a lawyer, you spend considerable time reviewing and interpreting legal documents. GPT can help with that. Its deductive and inductive reasoning skills and its understanding of procedural knowledge make it good at highlighting key clauses and potential issues or spotting similarities with past cases. But it can't make the final interpretation, especially when ethical reasoning or possible legal consequences are involved. That's when your expert judgment, based on your experiential and tacit knowledge, steps in.

### HR and Recruiting Assistant

Imagine an HR world where GPT helps screen resumes and cover letters. It uses inductive reasoning to identify potentially suitable candidates based on specific parameters. The AI can even answer common queries from applicants using its factual and procedural knowledge. But when it's time to pick the best fit for the company culture or evaluate softer skills, it's time for a human touch. Your emotional, intuitive, and ethical reasoning skills and experiential and personal knowledge of the company's needs and culture take center stage in final hiring decisions.

## Conclusion

Wow! Can you imagine the endless possibilities with this AI and human dream team? We're just peeking through the door to a world where our uniquely human experiences, intuition, and problem-solving skills merge with GPT's impressive reasoning powers and knowledge. It's like a magic key that can unlock any door! But hey! Let's remember the future of AI isn't just about all these cool gadgets and codes. It's about us humans with our knack for creativity and dreaming big. So, let's get our thinking caps on, roll up our sleeves and dive right into this thrilling new chapter of AI development!


Why Your Business Needs to Blend Human Expertise and ChatGPT


This blog post is the first in a series of articles to help bring full-stack engineers up to speed on LLMs and build LLM applications.

In this post, I'll explore what LLMs are, their evolution, the key concepts behind them, and some of their practical applications. By the end of this article, you'll have a high-level understanding of LLMs and why they've become such an essential part of modern AI applications. Let's dive in!

## What are Large Language Models (LLMs)?

Large Language Models are advanced artificial intelligence (AI) systems trained on massive amounts of text data to understand and generate human-like language. They can perform various language tasks, such as answering questions, summarizing text, translating languages, and even composing poetry.

Until recently, AI technology was primarily accessible to data scientists and machine learning engineers with specialized knowledge and skills. However, with the emergence of LLMs, AI has become more accessible to a broader developer audience. LLMs have simplified the process of building AI applications, enabling developers and even non-experts to create sophisticated and human-like interactions with computers. LLMs have opened up new possibilities for various industries and applications, making AI more inclusive and impactful.

## The Evolution of LLMs: From GPT to GPT-4

LLMs have come a long way since their inception. One of the most notable series of LLMs is the GPT (short for "Generative Pre-trained Transformer") series developed by OpenAI. Starting with GPT, each iteration has improved upon the previous one, leading to more powerful and versatile models.

The latest version, GPT-4, is an incredibly advanced model that can perform a wide range of language tasks with impressive accuracy. It has been trained on vast amounts of data, which allows it to generate highly relevant and contextually appropriate responses.

Apart from the GPT series developed by OpenAI, there are several other notable Large Language Models including, Google's PaLM and Meta's LLaMA family of models.

## Key Concepts Behind LLMs

A few critical concepts underpin LLMs, making them as powerful as they are. Don't worry if the technical jargon feels overwhelming! Let's take a look at some of the most important ones:

### Transformer Architecture

Think of transformers as the "brain" of LLMs. They help LLMs understand and create sentences by looking at all the words in a sentence at once, instead of one word at a time. A transformer helps LLMs understand the meaning of sentences better and faster.

### Attention Mechanism

Imagine that you're trying to understand a story your friend is telling you, but there's a lot of noise around you. You'll need to focus on your friend's voice to understand the story, right? The attention mechanism in LLMs works similarly. It helps the LLM focus on the essential parts of a sentence to understand better and generate language.

### Tokenization

Tokenization is like breaking a sentence into smaller pieces, like words or parts of words. LLMs do this to make it easier for them to understand and work with the text. It's similar to how we might break a big task into smaller steps to make it easier to complete.

## Overview of LLM Applications

LLMs have a broad range of potential applications thanks to their versatility and impressive language capabilities. Here are a few popular uses:

![Large Language Model Architecture](/images/blog/large-language-models.webp)
*Large Language Model (source: ArXiv)*

### Q&A Applications

You can build question-and-answer (Q&A) applications using LLMs that provide accurate and relevant information in response to user queries. For instance, if a user asks, "What are the health benefits of green tea?" an LLM-based Q&A application can generate a response detailing the various health benefits, such as improved brain function, fat loss, and reduced risk of heart disease.

### Text Summarization

LLMs can automatically generate concise and informative summaries of longer text documents. For example, given a news article about a recent election, an LLM can produce a summary highlighting the key points, such as the winner, the margin of victory, and the main issues debated during the campaign.

### Sentiment Analysis

LLMs can analyze the sentiment expressed in a text, determining whether it's positive, negative, or neutral. For instance, given a set of customer reviews for a product, an LLM can identify the overall sentiment towards the product by classifying each review's sentiment and summarising the results.

### Language Translation

You can also use LLMs to translate text between different languages accurately. For example, if you have a blog post written in English and want to share it with your Spanish-speaking audience, an LLM can automatically translate the text while preserving its meaning and tone.

These examples demonstrate the versatility of LLMs and their potential to address various language-related tasks across different domains and industries.

## The Importance of LLMs in Modern AI Applications

LLMs have transformed the field of AI and NLP, making it possible to create more sophisticated and human-like interactions with computers. Their ability to understand and generate natural language enables various practical applications across multiple industries, such as customer support, content creation, healthcare, and education.

In addition to making AI systems more robust, LLMs have made it easier for developers to build AI applications. With pre-trained LLMs, developers can fine-tune these models for specific tasks without extensive AI or language modelling knowledge.

## Wrapping Up

I hope this introduction to Large Language Models has given you a solid understanding of LLMs, their underlying concepts, and some exciting applications. As we continue this article series, we'll dive deeper into how to build LLM-based Q&A applications, system architecture, best practices, and advanced techniques.

Stay tuned for the next article in this series, where we'll focus on building Q&A applications using LLMs. We'll discuss the essential components of a Q&A system, selecting the right LLM for your application, fine-tuning for domain-specific tasks, and evaluation metrics. Happy learning!
