Product Management Fundamentals for AI

User journey mapping, capability layers, and system blueprinting.

1. The AI Product Lifecycle

1.1 Discovery & Definition

• Problem-Solution Fit: Is AI actually needed? (The "Why not RegEx?" test).
• User Personas: Who is the human in the loop?
• Value Prop: Speed? Quality? Novelty? Cost reduction?

1.2 Feasibility Analysis

• Data Availability: Do we have the data to train/finetune/RAG?
• Model Capability: Can current models actually do this? (POC first).
• Cost Modeling: Token costs vs User LTV.

2. Capability Layers

2.1 The "Magic" Layer (AI)

• What is the core AI task? (Generation, Classification, Extraction, Rewriting).
• What is the latency budget? (Streaming vs Batch).

2.2 The Control Layer (Guardrails)

• How do we prevent hallucinations?
• How do we handle refusals?
• Content moderation strategy.

2.3 The Application Layer (UI/UX)

• Skeuomorphic vs Native AI UI: Chatbot vs Autocomplete vs "Magic Button".
• Feedback Loops: Thumbs up/down, "Regenerate", "Edit".
• Latency Masking: Skeleton screens, streaming, optimistic UI.

3. System Blueprinting

3.1 Data Flow Mapping

• User Input -> Moderation -> Retrieval -> Prompt Assembly -> Model -> Parsing -> UI.
• Where is data stored? Who has access?

3.2 Evaluation Strategy

• Offline Eval: Test sets, Golden answers, LLM-as-a-Judge.
• Online Eval: Acceptance rate, Retention, Click-through.
• Feedback Loop: How does user usage improve the model?

4. Go-to-Market & Growth

4.1 Launch Strategy

• Waitlists: Manage demand and compute costs.
• Beta Testing: High-touch feedback from power users.

4.2 Monetization

• Usage-based: Pay per token/generation. Aligns cost with revenue. Hard to predict for users.
• Subscription: Predictable revenue. Risk of heavy users killing margins.
• Hybrid: Base sub + usage overages.