AI for Product Leaders

This blog gives an overview of the new initiative I'm part of: AI for Product Leaders. We aim to equip product leaders with the skills and insights needed to build successful AI applications. And build a community of product leaders who can learn, discuss, and grow together.

Why am I doing this course?

In this course, you will learn to build AI applications tailored for Product Leaders. Leveraging my experience as the CEO of Archie AI and former PM of Gemini for Google Cloud, I will guide you through the critical know-hows required to support and lead AI application development effectively.

At Google, I launched the industry's first GenAI experience for cloud infrastructure management. At Archie AI, I developed the first version of our application and managed all aspects of applied Large Language Model (LLM) experiences, including coding. This course distills my learnings from both these diverse environments to equip you with the skills and insights needed to build successful AI applications.

I'm hosting this course with Jinal Dalal, Director Engineering at Google. He has been leading engineering teams at Google for over 10 years and has deep experience in building AI applications.

Key Highlights:

• Practical Industry Experience: Insights from both a startup and a big tech environment.
• Concise Learning: Under 60 minutes to gain deep, actionable knowledge.
• Comprehensive Resources: Video lessons, slides, a polished handbook, and an open-source code repository.

Who Is This Course For?

Primary Audience: Product Leaders seeking a deep understanding of building AI applications.

Benefits:

• Time-Efficient: Complete the course in less than an hour.
• In-Depth Knowledge: Goes beyond surface-level information to explore underlying principles.
• Practical Insights: Learn from real-world experiences in both startups and large tech companies.

Why Take This Course?

While there's abundant content available online, there's often a significant gap when it comes to integrating all aspects of building an AI application. This course bridges that gap by providing a concise, comprehensive guide to AI product development, combining insights from both Google and Archie AI.

What You Will Gain:

• Holistic Understanding: From opportunity analysis to technical foundations.
• Unique Insights: Learn approaches that enhance your ability to support product teams effectively.
• Practical Tools: Access to video lessons, slides, a detailed handbook, and an open-source repository.

What This Course Includes

• Video Lessons: Under 60 minutes.
• Slides: Comprehensive presentation materials.
• Handbook: Detailed document as a reference guide.
• Code Repository: All the code discussed is available in an open-source repo: Auto-Review AI.

Course Content

1. AI Opportunity Landscape
2. Role in Building AI Applications
3. Technical Foundations
4. Hands-on AI Application Development
5. Defining Success and Production Readiness
6. Conclusion

1. AI Opportunity Landscape

AI represents a massive opportunity, with significant investments fueling its growth:

• VC Investment: $100B
• GPU Investment: $600B
• Revenue Projections: AI expected to create 200+ $1B revenue firms, compared to 20 from the cloud era.

Top AI Firms:

• GitHub Copilot ($100M ARR)
• Glean ($40M ARR)
• MidJourney ($200M ARR)

Key Differences in AI Product Development:

While the basics remain the same—defining problem statements clearly, conducting customer research, etc.—the experience, interface, and application building for AI are much different from what we're used to.

• Data-Centric Approach: Emphasis on data quality and relevance.
• Probabilistic Output: AI models provide probabilistic, not deterministic, outputs.
• Model Selection and Fine-Tuning: Choosing and customizing models is crucial.
• Explainability and Interpretability: Understanding AI decisions builds trust.
• Prompt Engineering: Crafting effective prompts impacts performance.
• User Expectation Management: High expectations require careful management.

This presents a massive opportunity for PMs to play a more critical role in product development like never before. It's one of those times when PMs should have opinions on how code is written—including prompting, chaining, and model selection.

2. Role in Building AI Applications

Opportunity Analysis

Not every AI opportunity makes sense for your business. You need to:

• Understand the Technology: Know what AI can and cannot do.
• Match Use Cases: Identify where AI adds real value.
• Consider Unit Economics: LLM experiences have inherent costs per use.

Filters for AI Opportunity (Example from Insights for Gemini Project):

• Accuracy Standards Aren't High:
  • For critical tasks (e.g., life and death), ensure a human-in-the-loop.
  • For less critical tasks, AI can automate or assist.
• Ease of Data Integration:
  • Bringing data to AI should be straightforward.

Experience Design:

Early LLM applications focused on chat tools, but limitations include:

• Feeling gimmicky or limited in scope.
• User fatigue with chat-based interactions.
• Challenges handling complex, multi-step tasks.

Notable AI UI Experiences:

• Cursor CMD+K and CMD+L: Seamless AI integration into the editor.
• Streaming Text with Auto-Scroll: Enhances user engagement.
• Google NotebookML: Mimics podcast experiences for topic discovery.
• Context Management Tools: Claude Artifacts, OpenAI Canvas.

Forms of AI-Based Application Experiences

• Embedded LLM Experience: Integrate AI into existing products (e.g., Google Search AI overview).
• AI Assistant (Augmentation): Focus on complex, meaningful tasks (e.g., AI-powered music composition).
• AI Automation: Automate small, bounded, repetitive tasks (e.g., email categorization).

3. Technical Foundations

Basics of LLMs:

• How They Work: Understanding the mechanics behind large language models.
• Strengths: What tasks LLMs excel at.
• Popular Providers: Overview of leading LLM providers.

Prompt Engineering Techniques:

• Few-Shot Prompting: Providing examples to guide LLM responses.
• Chain of Thought Prompting: Encouraging step-by-step reasoning.
• Role-Based Prompting: Directing the LLM to act in a specific role.
• Constrained Prompting: Structuring outputs for specific formats (e.g., JSON, XML).

Advanced Concepts:

• Agentic Workflow: Enabling AI agents to perform a series of actions.
• Vector Databases: Utilizing specialized databases for high-dimensional data (e.g., Pinecone, Qdrant).
• Key Terms: Embedding models, similarity search, and more.

4. Hands-on AI Application Development

Exercise Overview:

Objective: Build a Streamlit application to demonstrate AI capabilities.

Steps:

1. Start from a Notebook:

   • Install necessary libraries.
   • Define OpenAI client.
   • Demonstrate text input and output.

2. Code and Show Streamlit Demo:

   • Explain each code block's purpose.
   • Include components like LLM Class, review files, and Streamlit demo.

3. FastAPI Endpoints:

   • /review, /self-review, /audio-transcribe.

4. Deployment:

   • Dockerize the application.
   • Deploy to Google Cloud Run.

Additional Features:

• Integrate Helicone AI for tracking API calls and costs.
• Implement pricing tiers (Free vs. Paid).

Resources Provided:

• Open-Source Repository: Access to the auto-review AI repo.
• Step-by-Step Guidance: Instructions to follow along and build your own application.

5. Defining Success and Production Readiness

Measuring Success:

• Balanced Metrics: Combine short-term indicators with long-term value.
• Avoiding Pitfalls: Focus on meaningful metrics over vanity metrics.

Contextual Goals:

• Google Cloud Example: Customer satisfaction and value delivery.
• Archie AI Example: Reduction in PR Review Time

Production Readiness:

1. Content Safety and Moderation:

   • Utilize tools like Guardrails AI or Maxim AI.
   • Implement safety features from AWS Bedrock and Vertex AI.

2. Testing Practices:

   • Conduct extensive testing mimicking actual usage.
   • Implement automated test suites and canary deployments.
   • Identify and test edge cases.

3. User Experience Tracking:

   • Use tools like LangSmith or Helicone AI for logging and tracking.
   • Implement user feedback mechanisms (e.g., thumbs up/down).

4. Performance and Scalability:

   • Techniques to accelerate LLM responses.
   • Implement caching and manage token-per-minute limits.
   • Ensure version control and smooth deployment processes.

6. Conclusion

In this course, we've covered the essential aspects of AI application development tailored for Product Leaders:

• Massive Opportunity: AI requires a different approach to product development.
• Critical PM Role: From opportunity analysis to defining success metrics.
• Technical Foundations: Essential for effective collaboration with engineering teams.
• Hands-On Experience: Practical insights through AI tools and frameworks.

As you move forward, remember that the AI landscape is rapidly evolving. Stay curious, keep learning, and don't be afraid to experiment. Your role as a Product Leader in AI projects is more crucial than ever, bridging the gap between technical possibilities and user needs.

By applying the knowledge and skills gained in this course, you'll be well-equipped to lead AI initiatives and drive innovation in your organization.

Good luck on your AI journey!

Additional Information

Prerequisites Recap

• Software:
  • Python 3.7 or above
  • Streamlit installed
  • An editor like Cursor AI
• Preparation:
  • Think about an AI application you wish to build
• Resources:
  • Code Repository:
    • Auto-Review AI
    • Python Notebook
    • Slides

Interested in doing this course?

Get started with AI application development through our comprehensive course designed specifically for Product Leaders.

Questions?

If you have any questions or need assistance with the exercises, feel free to reach out via email (ajitesh@getarchieai.com). You can also join our Discord channel.