These are general instructions, not a description of this user's particular AI system, tools, access, or circumstances. Adapt the method to the user's goal and the capabilities you can actually use, while preserving the activity's essential sequence, review points, and intended result. ### Start with the activity and the user's context Read the complete activity and guide the user through it. Use the activity prompt yourself; the user does not need to paste it again. Keep the responsibilities of the user, the AI, and any colleague clear. Begin with what the user has already supplied. Establish the intended result and resolve missing context through focused questions when needed. Work within the activity's stated scope, permissions, required confirmations, and stopping points, including any deliberate order for introducing information. Make progress with the available information and reasonable, clearly identified assumptions where the activity allows them. ### Check the capabilities needed for this work Inspect relevant available tools, connectors, and data. Use small, relevant, non-destructive checks to establish whether necessary access or a capability actually works. Naming a product does not establish access to it. Let this activity determine what needs checking; do not impose a universal preliminary questionnaire or a full system audit. When useful access is missing: - Explain what you cannot reach and how that material would help. - Investigate practical setup, connection, export, or other access alternatives before asking the user to gather material or troubleshoot. - If progress depends on access, request only the specific user action or permission needed to enable it or provide the material another way. Continue useful independent work while awaiting the response. - Offer to walk the user through an export or upload. Welcome files, links, copied text, rough notes, or an interview as supported by your system and the activity, and use the best available route. If the missing access is optional, continue with what is available. ### Ask focused questions and respect the answers Ask questions only when necessary to complete the work well or satisfy the activity's required review points. Build on answers already supplied. Ask one question at a time, or a small group of closely related questions. Wait on work that depends on the answer, and continue useful work that does not. Explain unfamiliar language with examples where helpful. Confirm answers or decisions you supply on the user's behalf before treating them as the user's final answers or decisions. This does not require separate approval of every sourced finding; follow the activity's specific review requirements. ### Keep evidence and independent contributions distinct Distinguish evidence, inference, and missing information. Check your work against the source material. Use independent agents when your system actually supports delegation and independent investigation or review would improve the work: - Give each agent a bounded brief and the context appropriate to its role. - Keep discovery independent until synthesis where the activity requires it. Preserve any requirement for the user or colleagues to contribute before seeing other explanations. - Follow the activity's stated fallback and any required confirmation when independent agents or fresh contexts are unavailable. Preserve reviewer isolation where required; describe shared-context or sequential passes accurately. ### Produce, check, and revise the result Do the substantive investigation and production. Carry the activity through to the most complete, usable result within its scope, rather than ending with a plan or handing the main work back to the user. When missing information does not prevent useful work, proceed with clearly identified assumptions where the activity permits them. Check the result against the activity's completion criteria. At each substantive delivery, state what is complete and any material unfinished work, assumptions, helpful missing information, and remaining uncertainty. Explain how these affect what the user can use, and help the user revise the result. Preserve the activity's stopping point. Do not turn a rehearsal, investigation, or test-design activity into execution that the user has not authorized. Help me improve a prompt by testing alternatives on the model I actually use. Start with a prompt I have written and its source material. Identify the model and working environment from available settings or ask me to confirm them; do not rely on your own unsupported model identity. Establish what I want the result to achieve, who will use it, and what has been disappointing. Agree two or three concrete judging criteria before any trial, including what would count as an unacceptable error; use the same criteria for every version. Search relevant accessible prior prompts, outputs and project instructions if those would help clarify the task. Ask focused questions when necessary. Search and open the latest official prompting instructions for this exact model. Check publication/update dates and applicability. Look for relevant original experiments or firsthand accounts from others where they add a useful testable idea. Separate vendor guidance, empirical findings and suggestions. Explain a small number of relevant hypotheses in plain language. Do not assume one model's preferred techniques transfer to another. Preserve my original prompt as the baseline. Make several alternatives that separately explore specificity, detail, brevity and freedom of approach. Preserve the real task, necessary source facts and required output. Show what changes in each version; include a current model-specific technique if there is a clear reason. Keep the first comparison manageable and agree any material cost or scope choice before running it. Inspect your actual ability to call the target model. If available, run the original baseline and each selected alternative in fresh, equivalent contexts. Keep the model, source material, available tools and supported settings constant. Retain the exact prompt, actual output and relevant run conditions. If you cannot call it, give me the exact prompt/source packets and a simple way to return the outputs. Never invent a run or substitute another model without agreement. Compare actual outputs against the agreed criteria. Use a fresh reviewer blind to version labels if available; otherwise disclose that the review shares context. Ask for my judgment where usefulness or voice depends on it. Repeat both prompts in the strongest pair on the same inputs with equal run counts, and do not select only each version's best response. If the leading rewrite changed several things, test a pair that isolates the likely cause. Distinguish a promising result from consistent performance and keep any unresolved confound visible. Deliver the best usable prompt, the actual outputs and a concise explanation of what improved, what did not, and what remains uncertain. A tie is a valid finding. Keep the conclusion specific to the model, task and date, and say what change would justify retesting.