Every prompt. One page.

Write your honest answers to these four prompts. Short is fine. One sentence each is enough. Do not edit while writing.

1. The feeling I have about AI right now is [fear / skepticism / exhaustion / ethical objection / quiet curiosity / something else — name it].

2. The specific thing I am worried about is [your job, your kids, the planet, being scammed, looking stupid, being replaced, being left behind, the people who made it, something else — say the real one].

3. If I learn this tool and it turns out to be [useful / overhyped / dangerous / boring], what I will do differently is [your real answer, not the one that sounds smart].

4. The smallest honest commitment I can make right now is [I will do Lesson 1 / I will read Lesson 1 and stop / I will try one prompt and quit if it feels wrong / I will give this one hour total / something else].

Then read all four back to yourself. Out loud if you can.

Explain in 200 words, plain English, what you are doing when I type a message and you respond.

Use a concrete analogy a 12-year-old would understand. Tell me one thing you are genuinely good at and one thing you are genuinely bad at. Do not use the words "transformative," "synergy," "revolutionize," "powerful," or "intelligence."

After you answer, ask me one question to check whether the explanation landed.

Round 1 · the bad prompt (intentional):
"Help me with [thing]."

Round 2 · the good prompt (template — fill in the brackets):

I need to [the exact task].

Context:
- [who this is for]
- [what came before this — paste the email / the meeting note / the doc, if any]
- [what success looks like for me]

Constraints:
- [length / tone / format]
- [anything I have to include or avoid]

Output:
- [give me a [draft / list / outline / version A and B]]
- [if you need anything else from me before drafting, ask now]

Give me 5 academic papers from peer-reviewed journals on [specific obscure topic — e.g. "the effect of bioelectric signals on cell differentiation in planarian regeneration"]. For each:
- exact title
- authors
- journal name
- year
- DOI or URL

Be specific. I will check.

Open a blank note (Notes app, paper, anywhere). Title it "My system prompt v1." Then fill in this template — answer each line in one sentence, max:

WHO I AM:
I am [your role / what you do]. I live in [city or context]. The work I bring to AI is mostly [the 1-2 categories you actually ask about].

HOW TO RESPOND:
Default length: [one sentence / one short paragraph / structured bullets — pick one].
Default tone: [direct and plain / warm but brief / formal — pick one].
When I ask for options, give me [number] and stop.
When I ask for a decision, recommend one and name the trade-off.

WHAT TO SKIP:
Skip preamble like "Great question." Skip closing offers like "Let me know if you want more." Skip disclaimers unless the topic is legal, medical, or financial.

WHAT TO ASK FIRST:
If a request is ambiguous, ask one clarifying question before answering. Otherwise proceed.

Once you have all four sections filled in, you have a v1 system prompt. Install it in your tool of choice (see drill steps), then run one real task through it.

(Re-use the original prompt that disappointed you. Then push back across three turns.)

Turn 2: "This answer is [specific complaint — too long / too formal / missed X / wrong tone]. Rewrite, fixing that. Keep [what was good]."

Turn 3: "Better. Now [next thing to fix]. Same constraint."

Turn 4: "One more pass. [Final polish — make it tighter / make it sound like me / cut the last paragraph]."

(This drill is on paper or a Note, not in the AI chat.)

List your last 10 AI uses (best estimate, doesn't have to be perfect). Categorize each into one of three buckets:

1. TRUST — pattern task (drafting, structure, rewriting). I should not verify; the time spent verifying is more than the risk of being wrong.

2. VERIFY — fact task (numbers, names, citations, claims). I should check before using.

3. DON'T USE AI FOR THIS — decision is too high-stakes for AI alone.

For each VERIFY item, write the one specific check you should have done. For each DON'T USE item, name what you should do instead.

(This drill is in your Notes app, not in AI.)

Create a note titled "AI prompts." Add three sections, each with:
- a clear, descriptive name (you'll grep for it later)
- the prompt body (with [bracketed slots] for the variable parts)
- one example of what to paste in

Pick the three tasks YOU actually repeat this month. Examples (don't copy unless they're real for you):

1. "Reply to a tough work email · firm but professional · 100 words"
2. "Summarize a long doc into 3 bullets a busy exec actually reads"
3. "Plan dinner this week from what's in the fridge · cheap · no recipes I don't have"

I want to map your refusal posture so I can prompt you better. I'll ask five questions in this thread, one at a time. For each one, please answer fully if you can, or if you can't, tell me plainly: (a) that you're declining, (b) which category of concern it falls into, and (c) whether a rephrasing would help. Don't pre-disclaim — just answer or explain the refusal.

Here's question 1: I'm helping [my elderly mother / a friend / myself] figure out whether [over-the-counter medication name] interacts with [other medication or condition]. What should we be aware of, and what should we ask the pharmacist?

I'm going to show you three examples of how I handle [recurring task — e.g., replying to "can we hop on a call" requests]. Read all three, notice what they have in common (tone, length, structure, sign-off), then produce a fourth response in the same style on the new input I give you at the end.

EXAMPLE 1
Input: [paste the original request or raw input from a past case]
My response: [paste what you actually sent / produced — your best version]

EXAMPLE 2
Input: [paste another raw input from a different past case]
My response: [paste the polished output you produced for that one]

EXAMPLE 3
Input: [paste a third raw input]
My response: [paste the polished output]

NOW DO #4
Input: [paste the new real thing you need handled today]
Your response:

I need to [the task — e.g. "write a year-end performance self-review" / "plan a 3-day weekend in Portland with my partner" / "decide whether to take a job offer at a competitor"].

Before you draft anything, ask me 5 questions you'd need answered to give me a useful first version. Number the questions. Wait for my answers before drafting.

(First, paste the document into chat — text only.)

I'm going to paste a document. Once I do:
1. Give me a 5-sentence summary.
2. List the 3 most important things a decision-maker needs to know.
3. List 2 things this document conspicuously does NOT say but should.

If the document is too long to paste, tell me and we'll switch to upload.

(This drill is on paper. The output is a decision, not a chat.)

Answer for yourself:

1. How many times did I hit a free-tier limit in the last 14 days?
   - Zero: stay free another month.
   - 1–3: stay free another month and pay closer attention.
   - 4+: consider paying for one.

2. What's the work I'm doing most?
   - Writing-heavy → Claude Pro
   - General + image generation + custom GPTs → ChatGPT Plus
   - Tied into Google Workspace / Docs / Gmail → Gemini Advanced
   - Coding-heavy → ChatGPT Plus or Claude Pro (both good)

3. What feature pushed me over?
   - Bigger context window → Claude Pro (huge window) or Gemini Advanced
   - Privacy (zero data retention) → Claude Pro (Anthropic's posture)
   - Tools / extensions / browsing → ChatGPT Plus
   - Workspace integration → Gemini Advanced

4. Am I willing to delete the other two tabs for 90 days?
   - Yes: pay.
   - No: stay free, you'll waste $20.

I'm attaching an image of [what the image shows — e.g., "an error message in my terminal" / "a chart from a report I need to understand" / "a handwritten recipe card" / "the back of my router" / "a contract paragraph I need plain-English"].

What I need from you:
1. Tell me what you actually see in the image — be specific about the text, numbers, or details that matter.
2. [The real ask — e.g., "Explain what's causing this error and how to fix it" / "Tell me what this chart is showing in plain English" / "Type out the recipe in a clean format I can save" / "Tell me which port I plug my computer into" / "Translate this paragraph into language a normal person understands"].
3. If the image is unclear or you cannot read part of it, tell me which part and what a better photo would look like.

I'm trying to decide between [option A] and [option B] for [the actual decision — career move, purchase, project direction, whatever]. Here's what I know: [2–3 sentences of context — what's pushing each way]. What I keep getting stuck on is [the specific friction point]. Don't give me a pros and cons list. Just ask me one question that would help me see this more clearly, and we'll go from there.

PROJECT INSTRUCTIONS (paste into the "Custom instructions" or "System prompt" field when creating your Project / Custom GPT / Gem)

You are helping me with [SPECIFIC RECURRING TASK — e.g., "drafting client update emails for my consulting work"].

About me / my context:
- Role: [YOUR ROLE]
- Audience for this work: [WHO READS THE OUTPUT]
- Tone I want: [e.g., warm but professional, no jargon, short paragraphs]
- Things I always need: [e.g., a clear subject line, a one-sentence summary at the top, next steps in bullets]
- Things to never do: [e.g., never use the word "leverage," never close with "Let me know if you have any questions"]

When I drop in raw notes or a request, your default move is to draft the output in the format above. If anything is missing, ask me ONE focused question, not a list.

If I say "looser" or "tighter," adjust verbosity. If I say "more [name]" use prior drafts I attach as the tone reference.

Build me a working [tool type — e.g., unit converter / regex tester / habit tracker / tip calculator / color palette generator / markdown previewer] as a single self-contained HTML file I can run by double-clicking. Specifics: [list 3 to 5 details about how YOU need it to work — e.g., "converts between fluid ounces, milliliters, and cups," "shows match count and highlighted matches in real time," "tracks five habits with a 7-day view and dark mode," "splits the tip three ways with adjustable percentages"]. Render it in the Artifact/Canvas panel so I can use it right now. Keep all CSS and JavaScript inline — no external files, no CDN dependencies. After you build it, also paste the full HTML in the chat as a code block so I have a copy outside the panel.

(This drill is in your terminal, not in a browser. If "terminal" is new to you, read this lesson and skip the drill — come back to it at Operator level.)

1. Go to ollama.com. Download the installer for your OS.
2. Open Terminal (Mac) / PowerShell (Windows) / Terminal (Linux).
3. Run: ollama pull llama3.2:3b
4. Wait 2–5 minutes for the download (~2 GB).
5. Run: ollama run llama3.2:3b
6. You're now chatting with a local model. Type something. Press enter.
7. Try a real task you'd normally run on Claude / ChatGPT. Notice the speed, the quality gap, the privacy difference.
8. To exit: type /bye and press enter.

(Three tasks, three AIs. Same prompt template; different windows.)

Pick three tasks you have to do this week. They should be different in nature:
1. A writing task (email, draft, summary) → Claude
2. A research / fact task → Perplexity (or GPT with web on)
3. A Google Docs / Gmail / Calendar task → Gemini

For each: run the same prompt template from Lesson 2 (context / constraint / output). Note which AI felt right for that task.

I'm an operator-level AI user and I want to extend my AI's reach using Model Context Protocol (MCP) servers. Here is my actual stack:

Operating system: [Windows / Mac / Linux]
AI client I use most: [Claude Desktop / Claude Code / Cursor / ChatGPT Desktop / other]
Tools I use daily for work: [list 5–8 real ones — e.g. Gmail, Google Drive, Notion, GitHub, VS Code, Postgres, Figma, Slack, Linear]
The 3 tasks I do most often that involve copy-pasting between AI chat and another app: [list them]
What I'm not willing to plug in for privacy reasons: [e.g. personal banking, medical records, private journal]

Do three things:

1. Tell me which MCP servers exist for the tools in my stack. For each, give me: name of the server, who maintains it (Anthropic official / community / vendor), what it lets the model actually do, and any known sharp edges or auth gotchas.

2. Look at my three most-frequent copy-paste tasks and tell me which two MCP servers would eliminate the most friction. Be specific about why — name the actual operation.

3. Give me the exact install steps for those two servers on my operating system, including where the config file lives and what JSON I add to it. If a server requires an API key or OAuth, name the screen I have to visit to get the credential.

Don't recommend servers I didn't ask about. Don't pad. If you don't know whether a server exists for one of my tools, say so plainly instead of guessing.

I want you to act as a research agent on a real task. Here is the job:

GOAL: Find me [specific concrete thing — e.g., "the three cheapest currently-available 27-inch 4K monitors with USB-C 90W power delivery, in stock at US retailers"]

RULES, in order of priority:
1. Read-only mode. Do not add anything to a cart. Do not submit any form. Do not create an account. Do not click "buy" or "checkout" or "subscribe" under any condition.
2. Time budget: spend no more than [10] minutes on this task. If you have not found the answer in that time, stop and report what you did find.
3. If you hit a paywall, captcha, login wall, or any page that asks for payment info, stop immediately and tell me which site and which step.
4. If the answer requires me to make a judgment call (e.g., "the cheapest" depends on shipping or warranty), pause and ask me before continuing.

REPORT FORMAT when you're done or when you stop:
- What you found (the actual answer, with source URLs)
- What sites you visited (full list, in order)
- What you would have done next if I'd given you 10 more minutes
- Anything that surprised you about the task

Begin.

Find the highest-rated [cuisine type, e.g. ramen] restaurant within a 10-minute drive of zip code [your zip]. Open Google Maps, sort by rating, look at the top 3 results that have at least 100 reviews. For each one, scroll the recent reviews and tell me the three most common complaints. Do not click any phone numbers, do not start any directions, do not click any ads. Read-only. Report back with: name, rating, review count, and the three complaint themes per restaurant. Stop and ask me before doing anything that is not reading or scrolling.

I'm doing a self-audit of where AI does and doesn't belong in my work. I'll paste a list of 5 tasks from my current week. For each, I'll give my one-sentence reason it requires human judgment. Your job: push back honestly. For each task, tell me (a) is the human-judgment reason real, or is it ego/habit? (b) what part of this task could AI actually do well, even if a human still owns the final call? (c) what would have to be true for this to become safely delegatable in 6 months? Be direct. I want to know the line, not be flattered. Here are my 5 tasks: [paste 5 tasks with your one-sentence reason for each]

I want to design an AI agent (not a one-shot prompt) for this recurring task I do: [DESCRIBE THE TASK IN 2-3 SENTENCES]. Walk me through: 1) the minimum tools this agent would need (3-5 max, with one-line descriptions of each), 2) the stop condition that tells the loop 'we are done,' 3) the max-step budget you would set as a hard cap, 4) the failure mode you are most worried about, and 5) a brutally honest verdict: does this task actually need an agent loop, or could a single well-crafted prompt plus my own follow-up handle it? Do not flatter the agent framing. If single-shot wins, say so.

I am auditing my installed MCP servers. For each server I list below, tell me: 1) what tools/resources it exposes (group them: read-only, write, destructive), 2) what credentials or scopes it requires, 3) what the worst-case blast radius is if the model called the wrong tool, and 4) whether the server is from a verified publisher or a community repo. My installed servers: [PASTE LIST OF SERVER NAMES]. After the per-server audit, give me a one-line keep/review/remove recommendation for each, based purely on whether the value I get justifies the surface area.

I want to create a reusable skill primer for this task I do often: [DESCRIBE THE TASK · e.g. 'reviewing my marketing copy for brand voice,' 'triaging incoming support tickets']. Help me draft the skill file with: 1) a 60-character description that makes the firing condition obvious (when should this skill activate?), 2) a 200-word body that gives a cold AI session everything it needs to do this task my way (my context, my voice rules, my done-criteria, my common mistakes to avoid), 3) one example input and one example correctly-handled output so the model has a concrete pattern. Write it tight. No filler. No 'I hope this helps.' I will paste this into a skill file and use it for years.

Walk me through installing Ollama on [YOUR OS · macOS / Windows / Linux] and pulling one mid-tier model suitable for my hardware: I have [RAM AMOUNT] of RAM and [APPLE SILICON / NVIDIA GPU / CPU ONLY]. Recommend one specific model name to start with (pin the version), give me the exact pull command, and the exact run command to start a chat. Then give me one privacy-sensitive prompt I should try first to feel the difference · something I would not want logged to a cloud API. Skip the marketing about open source · just the install steps and the first real use.

I am calibrating when to use vision input. Here is a real task I do: [DESCRIBE THE TASK · e.g. 'debugging why a webpage looks wrong,' 'extracting data from a chart in a PDF']. I will try this three ways and want your honest verdict each time: 1) text-only · I describe the situation in words, you respond. 2) image-only · I paste a screenshot with no description, you respond. 3) text-plus-image · I paste the screenshot AND describe what I want, you respond. For each round, tell me what you can and cannot see clearly, and what you would need from me to do better. After all three, give a one-paragraph verdict on which mode wins for tasks of this shape.

I want to build a simple audio-to-artifact pipeline for this recurring audio I capture: [DESCRIBE · e.g. 'my Tuesday 1:1 with my report,' 'voice memos I record while walking,' 'a podcast I want to extract quotes from']. Walk me through: 1) the simplest recording setup that works on [YOUR DEVICE], 2) whether I should use local Whisper or a cloud API given my privacy needs of [DESCRIBE: high / medium / low], 3) the exact prompt I should run on the transcript after to get my downstream artifact (action items / summary / quote list / etc.), 4) one warning about what this pipeline will NOT capture well. No 'just use this app' hand-waving · give me actual commands or actual tools.

I am deciding between RAG and long context for this real corpus of mine: [DESCRIBE · e.g. '400 of my journal entries,' '12 PDFs of legal docs,' 'all my Slack messages from one channel for the year']. Help me decide: 1) what is the approximate total token size of this corpus (rule-of-thumb me an estimate), 2) does it fit in a 200K context window? a 1M? 3) at query time, do I typically need the whole thing or a small slice? 4) what is the worst-case if retrieval grabs the wrong chunk · would I notice? Based on those answers, give me a verdict: pure long-context, pure RAG, or hybrid · and the simplest possible first implementation for the winning strategy.

I want to feel how embeddings work with my own data. Walk me through the smallest possible end-to-end demo: 1) recommend one specific embedding model + API I should use for hobby-scale work (cost-aware), 2) give me a 20-line Python (or Node, whichever I pick · I prefer [LANGUAGE]) script that embeds these five short text snippets I will paste in: [SNIPPET 1 · e.g. about a topic] [SNIPPET 2 · related topic] [SNIPPET 3 · unrelated topic] [SNIPPET 4 · related to 1 and 2] [SNIPPET 5 · totally different domain], 3) computes which snippet is most similar to a query I will provide, 4) prints the similarity scores. Include the exact pip/npm install command. No 'just use LangChain' · I want to see the actual API call.

I am wondering if I should fine-tune a model for this task I do often: [DESCRIBE THE TASK · e.g. 'classifying customer emails into 8 categories,' 'writing in my specific voice']. Walk me through the honest decision: 1) what is the volume · how many times per week do I do this task? 2) what does failure cost me · is a wrong answer expensive or trivial? 3) how many clean labeled examples do I have, today, in a form a training script could consume? (be brutal about 'clean'). 4) what would I try first that is cheaper than fine-tuning · prompt-only, few-shot, RAG, or skill? 5) give me a verdict and the cheapest alternative I should try BEFORE I touch fine-tuning. Do not flatter the fine-tuning idea if it does not earn it.

I am building a personal AI-paste safety checklist tailored to my actual life. Help me list, specifically and honestly: 1) what categories of information I handle that I should never paste into a hosted AI (think: medical, financial, NDA-covered, third-party secrets, others' PII), 2) for each category, what the realistic blast radius is if it leaked (regulatory? professional? relational? legal?), 3) the safer alternative for each category (local model? redact-then-paste? do-not-use-AI-here?), 4) a one-line gut-check question I can ask before every paste · short enough to actually use. My context: [BRIEF DESCRIPTION OF YOUR WORK · e.g. 'solo founder building a fintech app,' 'therapist with private practice,' 'engineer at a company with strict IP policy']. No abstract advice · I want my checklist.

I want to practice multimodal prompting on a real question I had this week. The question is: [PASTE OR DESCRIBE THE REAL QUESTION]. The relevant artifact (if any) is [SCREENSHOT / DIAGRAM / RECORDING / PHOTO]. I will ask the same question three ways and want your honest critique each time: Round 1 · text-only, describing the situation as precisely as I can. Round 2 · attach the artifact with minimal text ('what do you see here?'). Round 3 · attach the artifact AND write a sharp text frame around it (here is what I expect, here is what I see, here is what I want to know). For each round, tell me what helped, what was missing, and what I should have included. End with a one-line rule of thumb for when to reach for each mode in tasks of this shape.

I want to feel the difference chain-of-thought makes. Here is a real problem I am working through: [DESCRIBE A REAL MULTI-STEP PROBLEM YOU HAVE · e.g. a planning decision with constraints, a debugging puzzle, a math word problem, an ambiguous policy question]. I will ask you the same question two ways and want your most honest work each time: Round 1 · 'What is your answer? Just the answer, one or two sentences.' Round 2 · 'Walk me through this step by step. List the constraints first, then the implications, then your conclusion. Show every step.' After both rounds, tell me: did the reasoning in Round 2 catch anything Round 1 hid? Was there a step in Round 2 you are least confident about? Where would you most want me to push back?

I want to learn structured output / tool use end-to-end with a real task of mine. The task: take this messy unstructured input · [PASTE A REAL EXAMPLE · e.g. a recipe in prose, a casual meeting note, a free-form support email] · and turn it into structured JSON I could store in a database. Walk me through: 1) propose the JSON Schema (3-7 fields, with types and which are required), 2) write the exact prompt I would send to the model to extract those fields from input like mine, 3) show me one valid output and one likely-invalid output (so I know what to guard against), 4) give me a 10-line Python or Node validator using a real schema library (Pydantic or Zod, whichever I want · I pick [LANGUAGE]). End with one concrete edge case I should test against.

I want to audit and optimize my AI spend. Help me work through it: 1) walk me through how to pull my last month's usage from [WHICH PROVIDER · Anthropic / OpenAI / both / other], grouped by workflow if possible, 2) identify which one workflow is most likely eating my budget given my description: [BRIEFLY DESCRIBE YOUR USAGE · e.g. 'I run a 100-line prompt against Claude Sonnet maybe 30x per day for content review'], 3) for that workflow, recommend a cheaper model in the same family and predict where the quality might drop, 4) walk me through whether prompt caching applies to my pattern · if yes, the rough savings; if no, why not. End with three concrete actions I should take this week to cut my AI bill by 30%+ without dropping the work I actually need quality on.

(This is a self-audit on paper. The output is a decision.)

Open your AI tools right now. Count:
1. How many chats / conversations do you have open across all AI tools?
2. How many of them are about ONE project you're working on (vs. unrelated)?
3. When was the last time you re-pasted project context into a new chat?
4. If you had to reconstruct what AI did for you last Tuesday, could you?
5. Are you running 2+ AI tools in parallel for one project?

If you answered 4+ to #1, 2+ to #2, "this week" to #3, "no" to #4, OR "yes" to #5 — you're at the Pilot threshold. The chat box is no longer enough.

If those answers are mostly the other direction, stay at Operator level. The cockpit will be the right tool when you actually feel the friction. Forcing the upgrade burns money you didn't need to spend.

I need to build a personal audit trail for my AI use. Help me design something I'll actually maintain.

Here's a recent example I want to turn into a receipt:
- What I was working on: [describe the task — e.g., "drafting a client proposal," "summarizing a research paper," "writing release notes"]
- Which AI tool I used: [Claude / ChatGPT / Gemini / Copilot / other]
- Approximate date: [date]
- What I asked it (paste prompt if you have it, or describe): [prompt or summary]
- What it gave me back (short summary): [output summary]
- What I did with the output: [shipped as-is / edited heavily / rewrote / threw away]
- How much time I think it saved me (honest guess): [minutes/hours, or "none / negative"]

Do three things for me:

1. Turn the above into a clean receipt entry I could paste into a notes file. Keep it under 10 lines. Use plain text, no markdown tables.

2. Propose the lightest possible system I could maintain — folder structure, file naming, or a single spreadsheet. I want something a busy person will still be doing in six months, not something elaborate. Pick ONE recommendation, not three options.

3. Give me five honest questions I should ask myself at the end of each month when I look back at my receipts. The questions should help me notice if I'm overpaying for AI, underusing a tool, or editing so much that AI isn't actually helping.

Push back if any of my inputs are vague. Ask me to sharpen them before you write the receipt.

You are a patient tutor for a [GRADE] student studying [SUBJECT — e.g., 6th-grade pre-algebra, 4th-grade reading comprehension, 9th-grade biology].

The student is working on this specific topic: [TOPIC — e.g., solving for x in two-step equations, identifying main idea vs. supporting detail, the difference between mitosis and meiosis].

Rules for this session:
1. Do not give direct answers. If the student asks for the answer, redirect to a question that helps them figure it out.
2. Ask one question at a time. Wait for their response.
3. When they get something wrong, do not tell them they are wrong. Ask them to walk through their reasoning, and let them find the mistake.
4. Keep your replies short — two or three sentences max — so it feels like a conversation, not a lecture.
5. End the session by asking the student to explain the concept back to you in their own words.

Start by asking the student what they already know about [TOPIC], and what they find confusing.

I am making this decision: [one-sentence decision, e.g. "Should I hire [name] for the [role] position at [salary]?" or "Should we ship [feature] in [timeframe]?" or "Should I sign [contract] with [counterparty]?"]

Context you need: [3-5 sentences of relevant background — what I know, what's at stake, what the timeline is, who is affected].

I want to think about this the way a senior engineer reviews code. Please answer all five questions below in order, with real depth on each — not summaries. Push back where you should.

1. What are the actual tradeoffs of this decision? Not pros and cons — tradeoffs. What am I giving up to get what I want?

2. What assumptions am I making that, if false, would make this decision wrong? List the load-bearing assumptions and rate each one's fragility.

3. Give me three alternatives I should be comparing this against. Rank them. Explain why the ranking goes that way.

4. Describe the worst-case version of executing this decision. What does the failure look like in detail, six months out?

5. What does someone who hates this idea say about it? Steelman the strongest critic. Do not soften their voice.

Before I read your answer, I am committing to one rule: I will write down what would actually change my mind on this decision. So tell me — what evidence in your answer should make me reconsider?

I have a long document I work with: [DESCRIBE · e.g. 'a 60-page contract,' 'a 200-page technical manual,' 'six months of journal entries']. Estimated total tokens: [ESTIMATE]. I have a recurring question I ask against it: [DESCRIBE THE QUESTION]. Walk me through three strategies, with real cost math: Strategy A · dump the whole document into a single long-context prompt with the question. Strategy B · chunk the document by section, retrieve the 3 most relevant chunks, send those plus the question. Strategy C · use prompt caching on the full document and ask the question against the cached version (assume I will ask 10+ similar questions). For each strategy, give me: estimated input tokens per query, estimated cost per query, expected accuracy tradeoffs, and a verdict on which strategy I should use given my query pattern.

I want to honestly evaluate one of my current AI workflows against open-weight alternatives. The workflow: [DESCRIBE · e.g. 'I use Claude Sonnet to draft replies to customer support emails, ~40 per day']. Walk me through: 1) what specific open-weight model would I try as the closest substitute (name a version that runs reasonably on [YOUR HARDWARE])? 2) where will the capability gap likely show up · which kinds of inputs will the open model handle worse? 3) what does the privacy / cost / latency comparison look like in real numbers for my volume? 4) is there a hybrid · open-weight handles the easy 80%, closed-weight escalates the hard 20%? 5) realistic verdict: should I make this switch, run a pilot, or stay where I am? Do not flatter open weights if they do not win for my use case.

I want to build the minimum-viable receipts layer for one of my AI workflows. The workflow: [DESCRIBE · e.g. 'a Python script that calls Claude to classify customer emails']. Walk me through: 1) the exact JSON schema for one receipt record · timestamp, model id with version, full input, full output, tools called, token counts, cost estimate, stable UUID, 2) the smallest code change I can make to start logging every call to a local file (JSONL · one JSON object per line) without restructuring my code, 3) one query I can run a week from now to answer 'show me every time the model output was longer than 500 tokens last week,' 4) what I should add to the schema if my use case ever needs compliance audit (PII redaction notes, user consent flag, retention policy). Show me code, not abstractions.

I want to set up a self-voice-clone workflow responsibly. Walk me through: 1) one or two reputable services in 2026 that REQUIRE identity verification before cloning (no anonymous-upload services), comparing them on price and consent enforcement, 2) the exact setup steps · what sample length, what to record, how the verification step works, 3) one real practical use case I should try first to feel the value (narrating a blog post? a voice intro to a portfolio? a long-form thank-you note?), 4) the rules I should write for myself about when I will NOT use my clone (e.g., live deception, signing contracts, deepfake content), 5) the security practices around the model file itself · who has access, what's the revocation path. Treat this like setting up a credential I will own for years.