Technical PM Skill

Apply a structured framework to technical PM questions targeting AI product roles.

When to Use

• User asks about RLHF, fine-tuning, evals, inference, model architecture
• User asks "Design a system that uses LLMs to X"
• User asks "How would you build a RAG system for X"
• User asks about technical trade-offs in AI/ML systems
• User asks about API design for AI products
• User says /technical-pm followed by a question
• Any question requiring ML/AI technical depth from a PM perspective

Context

• Tuned for: AI product roles at frontier AI companies
• What matters: Going deep with researchers and engineers. You don't need to implement, but you need to understand the technical landscape well enough to make informed product decisions.
• Common pitfall: Hand-waving on technical details. Be specific about architectures, trade-offs, and constraints.

Framework: AI PM Technical Method (6 Sections)

Section 1: Technical Clarifications & Constraints

Before designing anything, scope the technical problem:

• Capability Assumptions: What model capabilities are available? (reasoning, multimodal, tool use, code gen)
• Scale: How many users/queries? What latency requirements?
• Infrastructure: Cloud vs. on-prem? What compute budget?
• Data: What training/eval data exists? Privacy constraints?
• Integration: What systems does this need to plug into?
• Timeline: MVP vs. production-grade?

Section 2: Users (Developer & End-User Personas)

For technical products, think about two user layers:

• Developers/Engineers: Who builds on this? What's their skill level? What do they expect?
• End Users: Who consumes the output? What quality bar do they need?

For each persona: current workflow, technical sophistication, key frustrations.

Section 3: High-Level System Design

Draw the system architecture (describe it clearly):

• Data Pipeline: How does data flow in? (user input → preprocessing → model → postprocessing → output)
• Model Layer: Which model(s)? Foundation model + fine-tuned? Routing? Ensemble?
• Orchestration: How are multi-step workflows managed? (agents, chains, state machines)
• Storage: What needs to be persisted? (conversation history, embeddings, user preferences, model artifacts)
• Serving: How is inference served? (batch vs. real-time, edge vs. cloud)

For RAG systems specifically:

• Document ingestion pipeline (chunking strategy, embedding model, vector DB)
• Retrieval (similarity search, reranking, hybrid search)
• Generation (context window management, prompt engineering, citation)
• Evaluation (relevance, faithfulness, answer quality)

For Agent systems specifically:

• Tool/function calling architecture
• Planning and reasoning loop
• Memory (short-term working memory vs. long-term)
• Safety/sandboxing (what can the agent actually do?)

Section 4: Deep Dive & Trade-offs

The interviewer will pick an area to go deep. Be prepared for:

The Latency-Cost-Quality Triangle: Every AI system has this fundamental trade-off:

• Latency <-> Quality: Faster responses = less reasoning time, fewer model calls
• Cost <-> Quality: Cheaper inference = smaller models, less compute per query
• Latency <-> Cost: Real-time serving = more provisioned capacity, higher cost

Discuss specific techniques for each trade-off:

• Latency: Streaming, caching, speculative decoding, model distillation, edge deployment
• Cost: Batching, model routing (small model for easy queries, large for hard), quantization, spot instances
• Quality: Chain-of-thought, self-consistency, retrieval augmentation, fine-tuning, human-in-the-loop

RLHF Pipeline (know this end-to-end):

1. Supervised Fine-Tuning (SFT) on high-quality demonstrations
2. Reward Model training from human preference comparisons
3. PPO optimization against the reward model with KL penalty
4. RLHF alternatives: DPO (Direct Preference Optimization), RLAIF, Constitutional AI

Evals (increasingly critical for AI PMs):

• What to eval: Accuracy, safety, instruction-following, hallucination, code correctness
• How to eval: Human eval, LLM-as-judge, automated benchmarks, A/B testing in production
• Eval pitfalls: Benchmark contamination, Goodhart's law, distributional shift
• Building eval sets: Golden datasets, adversarial examples, edge cases, domain-specific

Context Windows & Memory:

• Trade-offs of larger context: Cost (quadratic attention), latency, lost-in-the-middle
• Strategies: Summarization, RAG, hierarchical memory, sliding window
• When to use fine-tuning vs. in-context learning vs. RAG

Hallucination Detection & Mitigation:

• Detection: Confidence calibration, self-consistency checks, retrieval verification, citation validation
• Mitigation: Grounding in retrieved facts, chain-of-thought transparency, abstention (model says "I don't know")
• Measurement: Factual accuracy benchmarks, human annotation, automated fact-checking

Section 5: API Design & Developer Experience

For platform/API products, design the interface:

• API surface: REST vs. streaming vs. SDK. Key endpoints.
• Developer journey: Sign up → first API call → production integration
• Documentation: What developers need to succeed
• Pricing: Per-token, per-request, tiered, seat-based
• Rate limiting & quotas: Fair usage, abuse prevention
• Versioning: How to ship improvements without breaking existing users

Section 6: Metrics (Technical + Product)

Technical metrics:

• Time to First Token (TTFT)
• Tokens Per Second (TPS)
• Error rate (4xx, 5xx, timeout)
• Cost per 1K tokens (input/output)
• Model accuracy on eval suite
• Hallucination rate
• Safety violation rate

Product metrics:

• Developer activation (first API call within 7 days)
• API adoption (monthly active developers, production integrations)
• Quality satisfaction (developer NPS, support ticket volume)
• Revenue (API spend, conversion to paid tiers)

Key Technical Topics to Know

Transformers & Attention

• Self-attention mechanism, positional encoding
• Scaling laws (Chinchilla, compute-optimal training)
• Multi-head attention, KV cache

Training Pipeline

• Pre-training (next token prediction on massive corpus)
• Supervised Fine-Tuning (SFT)
• RLHF / DPO / Constitutional AI
• Mixture of Experts (MoE) architectures

Inference Optimization

• Quantization (INT8, INT4, GPTQ, AWQ)
• Speculative decoding
• KV cache optimization
• Batching strategies (continuous batching)
• Model distillation (larger → smaller model)

Safety & Alignment

• Constitutional AI
• Red teaming and adversarial testing
• Content filtering and classifiers
• Responsible scaling policies

Multimodal

• Vision-language models (image understanding)
• Speech/audio models
• Video understanding
• Cross-modal retrieval

Output Format

Structure as a technical walkthrough. Be technical but accessible — translate between researchers, engineers, and product. Whiteboard-style system diagrams described in text. Aim for ~2500 words.

Research-First Workflow

Before generating the answer:

1. Research — Use web search to find latest technical thinking from AI leaders, engineering blogs from major AI labs, papers, benchmarks. Do 5-10 searches.
2. Cite sources — Include [linked source](url) inline for technical claims and architecture decisions.
3. Display the complete structured answer.

What Good Looks Like

• Starts with technical scoping questions (constraints, scale, data)
• System design is coherent and production-aware (not just academic)
• Understands the Latency-Cost-Quality triangle deeply
• Can explain RLHF, evals, RAG without hand-waving
• Shows awareness of what's hard (hallucination, eval, safety)
• Trade-off analysis is specific and quantitative
• Connects technical decisions back to user/product impact