Prompt engineering
Writing, improving and testing prompts for any AI assistant.
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- Improve a prompt
Diagnoses why a prompt gives weak or inconsistent results and rewrites it with clear context, task, constraints and output format while keeping its intent. Use on any prompt for any AI assistant.
- Adapt a prompt for a reasoning model
Rewrites a prompt for reasoning-capable models by removing step-by-step micromanagement, stating goals, constraints and success criteria, and keeping the output format exact.
- Adapt a prompt for a small model
Rewrites a prompt for a small or fast model with one job per call, simpler steps, explicit formats and more examples, plus a test that shows whether quality holds against the original.
- AI literacy coach
AI literacy coach who teaches people to use assistants well and critically - what they are good at, how they fail, how to verify outputs and protect privacy. Use for learning to work with AI.
- Audit a prompt for bias
Audits a prompt for biased framing, stereotyped examples, proxy attributes, exclusionary assumptions and unequal treatment across groups, then suggests neutral rewrites and paired tests.
- Build an AI output review checklist
Builds a checklist a team uses to review AI outputs before use, tiered by risk, covering facts, sources, numbers, tone, privacy, rights and sign-off. For teams adopting AI in daily work.
- Build a prompt from input and output examples
Reverse-engineers a reusable prompt from input and output example pairs, naming the implicit rules, format and edge cases, then dry-runs the prompt against the examples, including held-out ones.
- Build a test set for a prompt
Builds a hand-run test set for a prompt with happy, edge and negative inputs, expected behaviour and checkable pass criteria per case, and a scoring sheet to compare prompt versions side by side.
- Build a team prompt library
Designs a shared prompt library for a team - structure, naming, an entry template with variables, ownership, review and versioning - and drafts the first entries for the team's top use cases.
- Choose a model tier for a task
Picks a first-choice model tier and reasoning setting for a task from its difficulty, volume, latency, cost and risk, with signs to step up or down and a check sized to the stakes. No model names.
- Compress a prompt
Shortens a long prompt while preserving its behaviour, maps every original instruction to where it now lives, reports the real size reduction and lists test inputs to check nothing changed.
- Convert an SOP into a prompt
Converts a standard operating procedure into assistant or agent instructions with ordered steps, decision rules, checks, escalation triggers, a gap list and test scenarios traced to the SOP.
- Create few-shot examples
Builds a small set of diverse, representative few-shot examples for a task, including tricky and negative cases, balanced so the model learns the rule rather than copying surface patterns.
- Design a prompt chain
Splits a complex task into a chain of focused prompts with defined inputs and outputs, checks between steps, failure handling and a test plan. Use when automating multi-step work with AI.
- Diagnose prompt failures
Diagnoses why a prompt produces bad answers from failing examples, traces each failure to a root cause, proposes targeted fixes and a quick regression test set.
- Harden a prompt against injection
Hardens a prompt or assistant against prompt injection from untrusted content with input separation, an instruction hierarchy, least-privilege actions, output limits and an attack test set.
- Learn prompting basics
Teaches prompting basics interactively on the learner's own tasks, one technique at a time, with before-and-after prompts, a short exercise and feedback. For beginners to AI assistants.
- Port a prompt to another model
Ports a working prompt to another model family or vendor, adjusting structure, examples, format instructions and call settings, with a parity test plan. For builders switching models.
- Practise spotting AI errors
Trains AI literacy with rounds of assistant-style answers containing planted errors, such as invented citations, wrong maths or outdated facts, and coaches the user to catch and verify them.
- Prompt engineer
Prompt engineer who writes clear, testable instructions, iterates against real examples and evals, and avoids model-specific tricks. Use for designing, debugging and maintaining prompts.
- Prompt iteration track
Improves a prompt in gated steps - define success, build test cases, run and grade, diagnose failures, revise, then compare versions on the same cases before adopting the change.
- Red-team a prompt
Tests a prompt or assistant setup against adversarial inputs - injection, edge cases, off-topic and harmful requests, data leaks - predicts failures and proposes fixes. For assistant builders.
- Run prompt regression tests
Runs a prompt test set against the current and candidate prompt versions with the project's command, grades outputs with the stated checks and reports regressions, wins and flaky cases side by side.
- Turn a chat into a reusable prompt
Turns a successful chat conversation into a reusable prompt with named variables, the rules learned from your corrections, an output format and a worked example. Use for tasks you repeat with AI.
- Write a batch processing prompt
Writes a prompt for processing many items consistently through an API or script, with a per-item output schema, stable labels, ID echo, error records and a QA sampling plan.
- Write a roleplay character prompt
Writes a roleplay character prompt with personality, voice samples, knowledge limits, boundaries and consistency rules. For interactive fiction, language practice, training and games.
- Write a classification prompt
Writes a classification prompt with sharp label definitions, include and exclude rules, boundary cases, balanced examples and an abstain option, plus a plan to measure accuracy on labelled data.
- Write a deep-research brief
Writes a brief for an AI deep-research run - precise question, scope, source rules, output format and how to judge the result - so a research agent investigates the right thing.
- Write an LLM-as-judge prompt
Writes an LLM-as-judge grading prompt with a calibrated scale, anchored examples for each score, ordered criteria and a structured verdict, plus checks for common judge biases.
- Write a long document prompt
Writes a prompt for analysing long documents with document placement, metadata tags, quote-first answering, citations, a not-found rule and a chunking plan for documents that do not fit.
- Write prompts for spreadsheet AI functions
Writes prompts for AI functions inside spreadsheet cells that classify, extract or rewrite each row consistently, with fixed outputs, blank handling, cell assembly and a spot-check plan.
- Write a structured output prompt
Writes a prompt that returns schema-valid JSON reliably, with a JSON Schema, field rules, examples, edge-case handling and a validate-and-retry plan. For extraction and app integrations.
- Write a system prompt
Writes a system prompt for a custom assistant from its purpose, audience, boundaries and tone, with handling for missing information and off-topic requests, plus a set of test questions.
- Write a reusable task prompt
Writes a reusable prompt from a plain description of a task, with context, typed variables and defaults, constraints, an output format, an example and a rule to ask for missing inputs.
- Write a system prompt for a voice agent
Writes a system prompt for a voice assistant or phone agent with short spoken turns, confirmation of key details, recovery from mishearing and silence, tool use and a graceful handoff to a human.
Not: coding-agent operations (meta).