AI Product Management Certification

Stop building "solutions looking for problems." Map user friction directly to AI capabilities.

Use the 5-Question Framework to quantify pain severity, frequency, and total market impact.

Draft an AI PRD that defines risks, guardrails, and success metrics before writing code.

Don't just add a "chat" button. Use the 3P Framework: Prioritization, Placement, Prominence.

Master the 4 AI Design Patterns: Inputs, Special Instructions, Outputs, and Feedback Loops.

Design for "non-determinism" using Human-in-the-Loop and Levels of Autonomy frameworks.

Prompting isn't guessing. Master the Context vs. Behavior Matrix to pick the right tool.

Know exactly when to escalate from Prompt Engineering to RAG or Fine-Tuning.

Apply advanced tactics: Chain of Thought, Few-Shot, and Delimiters to reduce errors.

"Vibe checks" don't scale. Build automated evaluation suites using LLMs as graders.

Create "Golden Datasets" to benchmark performance against specific user intent.

Run adversarial "Red Teaming" to catch hallucinations, PII leaks, and prompt injections.

Move beyond simple assistants. Implement orchestration patterns like the "Manager Role".

Master the Agent Stack: Sense (Multimodal), Plan (Reasoning), and Act (Tool use).

Solve reliability issues like "Task Looping" via state tracking & termination rules.

Stop defaulting to GPT-4. Evaluate models based on Time-to-First-Token and TPM cost.

Learn the trade-offs between proprietary models (OpenAI) and Open Source (Llama/Mistral).

Understand the "Fine-Tuning" trap: Why you likely don't need it, and when you actually do.