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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Not: coding-agent operations (meta).