PM AI Native

Figure out which AI tool actually fits the task in front of you — chatbot, coding assistant, image model, agent, or none — instead of forcing one tool onto everything. Use when asked which AI tool should I use for, what's the best AI for, do I even need AI for this, or should I use ChatGPT or something else. Produces a match between your task and the right kind of AI tool (with why), the trade-offs that matter for your case, when the answer is a non-AI tool or plain human effort, and how to try it cheaply before committing — so you pick by fit, not by hype or habit.

Decide what in your workload to hand to AI and what to keep yourself — like managing a fast, capable, but unreliable new hire — so you get leverage without offloading the things that need you. Use when asked what should I delegate to AI, what can AI take off my plate, where should I use AI in my work, or what should I keep doing myself. Produces a sort of your tasks into delegate-fully / delegate-with-review / keep-human, the reasoning behind each line, how to brief the AI on the delegated ones, and where your judgment is the actual value — a delegation map that frees your time without giving away your edge.

Level up how you actually use AI — from basic one-shot questions to the techniques that get dramatically better results — matched to what you already do. Use when asked how do I get better at using AI, how do power users use AI, I feel like I'm using AI at 10%, or teach me to use AI better. Produces an honest read of how you use AI now, the two or three highest-leverage techniques to add next (giving context, iterating, showing examples, breaking down tasks, verifying), a concrete before/after on your own use, and a simple practice path — so you close the gap between basic and expert without drowning in tips.

Figure out why a prompt isn't working and fix it — diagnose the actual failure (ambiguity, missing context, wrong format, conflicting instructions) instead of randomly rewording. Use when asked why isn't my prompt working, the AI keeps ignoring my instructions, my prompt gives inconsistent results, or how do I fix this prompt. Produces a diagnosis of the specific failure mode, the targeted fix for it (not a vibes rewrite), a corrected prompt, a check that it generalizes rather than fixing one case, and the principle behind the fix so you stop hitting it — turning prompt frustration into a debuggable, repeatable process.

Learn to recognize where and how AI tends to go wrong — the specific failure patterns — so you catch its mistakes on sight instead of getting burned by confident errors. Use when asked how do I know when AI is wrong, what are AI's common mistakes, how do I catch AI errors, or where does AI mess up. Produces the failure patterns most relevant to how you use AI (hallucinated facts, fake citations, outdated info, sycophancy, math slips, missed nuance), the tells that give each away, a quick check for the ones that would hurt you, and a calibrated trust level — so you develop the instinct to catch AI's errors before they cost you.

Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting. Use when asked why does AI give me generic answers, how do I give AI better context, my AI results are mediocre, or how do I get it right the first time. Produces the specific context this task needs (who/what/constraints/examples/format), a reusable primer you can paste ahead of the request, the difference between a starved prompt and a well-briefed one, and what to leave out — turning vague back-and-forth into a strong first result.

Design a small team of AI agents to tackle a complex task in parallel — who does what, how they hand off, and how to keep them coordinated — instead of one overloaded agent doing everything serially. Use when asked how do I use multiple AI agents, set up an agent team, orchestrate agents for, or run agents in parallel. Produces a decomposition of the task into agent roles, a coordination pattern (parallel vs sequential, how outputs combine), the context each agent needs (and what to keep isolated), a review/quality step, and the guardrails to keep it from going off the rails — practical multi-agent design for real tasks.

Check AI output before you trust or use it — where it's likely wrong, what to verify, and how to catch confident-sounding errors. Use when asked can I trust this AI answer, how do I verify what AI told me, fact-check this AI output, or is this AI response reliable. Produces a risk read on the specific output (the claims most likely to be wrong or made up), the parts that need independent verification vs the parts that are low-risk, how to actually verify each, the tells of AI hallucination and overconfidence, and a habit for building verification into your AI use — because AI is confidently wrong often enough that unchecked trust is a real risk.

Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally — so an agent that works in a demo becomes one that works in reality. For builders putting AI agents into real workflows.

Design an AI-assisted workflow for a recurring task — which steps to hand to AI, which to keep human, and how they connect — so you get leverage without losing quality or control. Use when asked how do I use AI for [process], automate this with AI, design an AI workflow, or where does AI fit in my process. Produces a map of the task's steps split into AI-does / human-does / human-checks, the right tool/prompt for each AI step, the hand-offs and review points, the failure modes to guard against, and a start-small rollout — turning a manual process into a reliable AI-assisted one that keeps you in control.

Set up a repo or project so an AI coding agent works well in it — the CLAUDE.md, the context, the guardrails, and the conventions the agent needs to be useful instead of lost. Use when asked how do I set up CLAUDE.md, configure my repo for Claude Code, my AI agent keeps getting my project wrong, or onboard an AI agent to my codebase. Produces a structured CLAUDE.md/project-context file (architecture, conventions, commands, do-nots), the right level of detail (enough to orient, not a novel), the guardrails that keep the agent safe (what not to touch, how to test), and a maintenance habit so it stays current — turning a repo an agent flails in into one it navigates like a teammate.

Build a personal library of reusable prompts for the things you ask AI again and again — so you stop rewriting the same request from scratch. Use when asked help me build a prompt library, save my best prompts, I keep writing the same prompts, or organize my AI prompts. Produces a captured set of your recurring AI tasks turned into reusable, parameterized prompt templates, an organization scheme so you can find them, guidance on what makes a prompt reusable (clear role, inputs, output format), and how to store and improve them — turning ad-hoc prompting into a personal toolkit that compounds.

Keep your AI memory/context file (MEMORY.md, CLAUDE.md, custom instructions) healthy over time — pruning the stale, adding the new, and keeping it sharp so your AI keeps getting you right. Use when asked review my memory file, my AI context is outdated, clean up my CLAUDE.md, or maintain my AI instructions. Produces a review of your existing memory/instructions file (what's stale, contradictory, bloated, or missing), edits to prune and sharpen it, additions from recent patterns worth remembering, and a light maintenance habit — because a memory file that isn't tended drifts from who you actually are, with a privacy check on what should never be stored.