Categories › Productivity & Office › Notes, docs & knowledge bases
Learn how AI products work: lessons, model cost comparisons, real experiments, PM guides.
Remote server: your requests and data go to aipmlab.io. Claude Code: claude mcp add --transport http ai-pm-lab https://aipmlab.io/api/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
search_lessons — Find AI PM Lab lessons on a topic (e.g. RAG, evals, agents, prompt injection, cost). Returns the best matches with their key idea and link.get_lesson — A lesson in full: the product decision it informs, its key idea and concepts, things to try with what to notice in each (from recorded runs)list_challenges — The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.define_term — A plain-language definition of an AI product concept (tokens, temperature, RAG, embeddings, LLM-as-judge, prompt injection…), with the lessocount_tokens — Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly).estimate_cost — What a model call costs per request, per day and per month at a given volume, from AI Gateway's live prices. Model ids look like 'openai/gptcompare_models — Compare several models side by side at a given volume: cost per request and per month, prices, context window, output limit, release date anlist_experiments — The real runs recorded on one of AI PM Lab's 3D pages or lessons, each described by its setup (e.g. which defences or settings were on). Repreplay_experiment — What happened in one recorded real run (e.g. whether a prompt injection leaked data with given defences, or how many answers were made up wiquiz_me — A round from AI PM Lab's challenges, built from real recorded runs: Call It (predict what a model did), Spot the Failure (ship or flag a reareview_ai_spec — Check an AI feature spec or PRD against what AI PM Lab teaches: approach, model and cost, prompt, grounding, output contract, tools, securitprep_planning_meeting — The questions a PM should ask engineers about a planned AI feature, with why each matters and what a good answer sounds like. Returns instrudesign_eval_suite — Build an evaluation suite for a planned AI feature with AI PM Lab's method: test cases (typical, edge, out of scope, attacks), graders with write_acceptance_criteria — Acceptance criteria for an AI feature or user story that can actually be tested: Given/When/Then with measurable pass bars on a test set, muprice_ai_feature — Work out an AI feature's unit economics and pricing: cost per request and per user (light, typical, heavy), levers like caching and routing,plan_ai_launch — A phased roadmap from where an AI feature is today to launch and its first month live: frame, prototype and model, evals, hardening, pilot, review_launch_readiness — A go/no-go review of an AI feature's launch plan: evals, grounding, attacks, approvals, cost, latency, monitoring, rollout and support, eachreview_ai_incident — A blameless post-incident review for an AI feature: where it failed (input, retrieval, prompt, model, tools, output checks), why evals and mNot tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.