Fine-Tuning Versus RAG: When to Use Each

Today’s Episode

The salary for AI PMs is skyrocketing.

Hamza Farooq works with companies like Home Depot, Trip Adviser, and Jack in the Box on their AI strategy. He teaches AI PM courses at Stanford, UCLA, and Maven.

Today, he’s giving you the complete 6-month roadmap to go from no experience to PM at OpenAI or Anthropic.

We built a live AI prototype in 30 minutes (with RAG and agents working). And Hamza breaks down the exact technical skills you need to master.

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Check out the conversation on Apple, Spotify and YouTube.

Brought to you by:

* Maven

* Amplitude: The market-leader in product analytics

* Vanta: Leading AI compliance platform

* NayaOne

* Kameleoon: Leading AI experimentation platform

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Key Takeaways:

1. AI PM salaries are skyrocketing - The median total comp for AI PMs is rapidly increasing. But now you need technical depth. Previously, you didn't need to know what RAG is or how fine-tuning works. Now you have to be a jack of all trades.2. We built a working prototype in 30 minutes - Live demo: Lovable for front-end + n8n for workflow automation + RAG connected and working. What used to take days now takes minutes. This is the power of modern AI PM tools.3. Context engineering is more important than prompt engineering - Prompt engineering is what you tell an LLM. Context engineering is how you design the instructions. You combine: system prompt, user prompt, memory (long-term), and RAG. This enables true personalization.4. Know the difference: fine-tuning vs RAG - Fine-tuning = adding new vocabulary (new words). RAG = adding new knowledge (new information). Use RAG for knowledge that changes frequently. Use fine-tuning for vocabulary or specialized response patterns.5. The 5-step architecture you need to master - Step 1: Understand what LLMs are. Step 2: Learn how to build applications. Step 3: Master prompt engineering. Step 4: Implement RAG systems. Step 5: Build agentic systems. Follow this roadmap on repeat.6. Use the three-wave approach for building - Wave 1: Save time (efficiency gains). Wave 2: Better quality (better output). Wave 3: Completely new (novel capabilities). Start with time-savers, progress to quality improvements, end with breakthrough innovations.7. Ask yourself 3 questions before building anything - Does it solve a user problem? Does it solve an organizational problem? Does it align with your business model? If yes to all three, build it. This validates every project.8. Build-first mentality wins - Don't just follow roadmaps. Keep building things. You have to learn by doing. The best way to become an AI PM is to build 10+ projects and see where your products fit in solving real business problems.9. Real-world example: Traversal.ai - Hamza's company works with manufacturers (Amazon suppliers, Jack in the Box, Home Depot). They built an army of agents processing 20,000 SKUs daily with demand forecasts. Results: better inventory optimization, planning, and cost savings.10. Teaching accelerates your own growth - Hamza makes 10-15% of revenue from Maven courses. Why keep teaching? "I teach because I grow." His foundation course builds empathy with users. His developer course uplifts his technical skills by working on real problems with senior engineers.

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Where to Find Hamza Farooq

* LinkedIn

* Newsletter

Related Content

Podcasts:

* Google AI PM Director drops an AI PM Masterclass

* If you only have 2 hrs, this is how to become an AI PM

* Complete Course: AI Product Management

Newsletters:

* How to Become an AI Product Manager with No Experience

* How to Write a Killer AI Product Manager Resume

* How to become an AI Product Manager

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