Claude prompts for AI product manager job searches

I am applying for the AI product manager role below. Rewrite my resume to match it using only experience that is already on my resume. Do not invent products, launches, metrics, users or teams I have not led. The posting may ask for shipping ML or LLM features, defining evaluation and quality metrics, working with research or data science, and handling model risk; for each requirement, find the closest real evidence in my resume, and if there is none, put it under "Gaps" rather than writing it in. Output: a table with the columns requirement, evidence, rewritten bullet; a revised one-page resume in plain text where each bullet names the product decision, what I did and the outcome for users or the business, with the number my resume already gives; and the Gaps list with one honest sentence per gap. Keep every job, title and date unchanged.

Job description:
[paste job description]

My resume:
[paste your resume]

Write a cover note of at most 120 words for the AI product manager role below. Use only facts from my resume and my notes. No invented results, no "passionate", no "transformative". Structure: one sentence that shows I understand the product and the specific problem an AI feature has to solve for its users (take it from the job description), two sentences on the most relevant product I have shipped and what changed for users or the business, with a number if I have one, and one closing sentence with a specific ask. Then give a 50-word version for a LinkedIn message to the hiring manager. Sentence case, plain words, no exclamation marks.

Job description:
[paste job description]

My resume:
[paste your resume]

Why this product, in my words:
[two or three lines]

Act as the interviewer for a 45-minute AI product manager screen at the company below. Ask one question at a time and wait for my answer. Cover, in order: a product sense question about one of the company's features (who it is for, what problem it solves, how you would know it is working), a question on when an AI approach is the wrong choice compared with rules or a simpler product change, a question on defining quality for a model that is sometimes wrong (what metric, what threshold, what happens on failure), a prioritization question with three competing requests, and a short question about the technical concepts I claim on my resume, at the depth a PM needs. After each answer, score it 1 to 5, give a strong answer, and name the one thing I should have said. End with three topics to review tonight.

Job description:
[paste job description]

My resume:
[paste your resume]

Run an AI product case interview with me for the role below. Pick a scenario that fits the company (for example adding a generated summary to a workflow, an assistant inside an existing product, or deciding whether to build a classifier that flags risky content) and describe it in three sentences. Guide me one stage at a time, waiting for my answer: the user and the job to be done, whether AI is the right tool, the smallest version worth shipping, what quality means and how to measure it before and after launch, the failure modes and what the product does when the model is wrong, data and privacy questions, cost per use and pricing, the rollout plan, and the success metrics with guardrails. After each stage, point out what an experienced AI PM would have raised that I missed. Finish with a one-page summary in my corrected words. Do not give the full answer up front.

Job description:
[paste job description]

What I know about the product:
[a few lines]

Help me prepare behavioral answers for an AI product manager interview using only the situations I describe below. Do not add details; if a story is missing an outcome or a number, ask me for it. First list the eight questions most likely for this role and company (a launch that missed its goal, a disagreement with engineering or research about scope, a model quality problem that reached users, saying no to a stakeholder, a decision made with incomplete data, a mistake you owned, influencing a team you did not manage, and cutting scope to hit a date). Then map each to one of my stories and write an answer of 150 to 200 words in the shape situation, action, result, what I would do differently. Flag where the same story is used twice so I can spread them out.

Job description:
[paste job description]

My stories, rough notes are fine:
[three to five real situations]

Turn my notes below into a product case study of 500 to 700 words about an AI feature I shipped, first person, plain language, aimed at a hiring manager. Use only what is in my notes. Where something important is missing (the user problem, the metric and its before and after values, the model or approach, the quality bar we set, what went wrong, what we cut), insert a placeholder in square brackets and list the placeholders at the end. Structure: the problem and the evidence it was real, the options considered and why AI, the smallest version we shipped, how we measured quality and what we did when the model was wrong, the result, what I would do differently, and what happened next. Suggest a title, a two-sentence summary for the top, three honest resume bullets, and one screenshot or diagram that would make it clearer.

Project notes:
[paste notes, PRD excerpts, launch reviews or a rough description]

I have an offer for an AI product manager role. Below are the offer details, my current compensation, any competing offers and what I have found from public sources. Help me evaluate and negotiate honestly; do not invent market data, and tell me what to look up if you need a number I have not given. Output: (1) a table of four-year total compensation under a cautious and an optimistic equity assumption; (2) which components are most negotiable for this kind of company and why; (3) a short script for the call that asks for a specific number with a reason tied to my experience, in a collaborative tone; (4) questions to ask before accepting, including the level, vesting, the scope I would own, who the engineering and research counterparts are, how product decisions get made, and how the role is measured in the first year. Under 500 words.

Offer:
[base, bonus, equity and vesting, signing bonus, location]

My situation:
[current compensation, competing offers, constraints]

Write a 30/60/90-day plan for me as a new AI product manager on the team below. Keep it realistic: I have to learn the users, the product, the model and its limits, the metrics and the people before deciding anything. Days 1 to 30: talking to users and support, reading the metrics and the quality dashboards, understanding what the model gets wrong and how often, and meeting engineering, research, design and legal. Days 31 to 60: a first decision, for example a quality bar written down, a roadmap draft agreed with engineering, or one small improvement shipped and measured. Days 61 to 90: a measurable outcome with a success metric and risks. For each block, give three to five outcomes rather than activities and one question to ask my manager in week one. Mark assumptions I should verify. Under 450 words.

Job description:
[paste job description]

What I learned about the team in interviews:
[notes]

Help me draft a one-page PRD for the AI feature described below, the way I would submit it in a take-home. Ask me first for anything you need: the user, the product, what data exists, and what a wrong output costs. Then produce a PRD with these sections, each short: the problem and evidence, the user and the job to be done, why an AI approach rather than rules or a simpler change, the smallest shippable version, what "good" means with a specific quality metric and a launch threshold, what the product does when the model is wrong or uncertain, data and privacy requirements, cost per use and the limit we would set, success metrics and guardrails after launch, open questions, and what we are explicitly not building. Keep it under 700 words and mark every number that is an assumption so I can replace it with a real one.

The feature:
[describe it]