You don’t need to code to lead in AI. But you do need to learn fast, think clearly, and build smart. The best product managers today aren’t ML PhDs—they’re translators between business, tech, and customers.
Here’s your roadmap to becoming one:
1. Basic Concepts
→ Understand the foundational ML/LLM concepts, architectures, and terminology.
Think: LLMs, Transformers, SLMs, SAMs…
2. Prompt Engineering
→ Learn how to effectively interact with AI models—your interface to power.
Think: Chain-of-thought, role prompting, system prompts, and constraints.
3. Fine-Tuning
→ Understand how models adapt to new tasks and data.
Think: SFT, DPO, LLaMA-Factory, Hugging Face AutoTrain.
4. RAG (Retrieval-Augmented Generation)
→ Combine knowledge bases and LLMs to create smarter systems.
Think: Vector DBs like Pinecone + document stores like Elastic.
5. AI Agents & Agentic Workflows
→ Build autonomous workflows using agents and orchestration layers.
Think: n8n, AutoGen, LangChain, LAMINI.
6. AI Prototyping & Building
→ Move from concept to testable product.
Think: No-code tools, IDEs, infra tools like Supabase, PromptLayer.
7. Foundational Models
→ Track and understand major AI players and their capabilities.
Think: Claude, ChatGPT, Gemini, Mistral, and more.
8. AI Evaluation Systems
→ Learn how to measure the quality and success of AI models.
Think: LLM Judge, human eval, TPR, A/B tests.
9. Other Resources
→ Get access to templates, guides, PRDs, and model directories to speed up your learning.
AI won’t replace Product Managers. But PMs who understand AI will replace those who don’t.
Tag someone who should be reading this. Let’s start building smarter.