Hire an AI prompt engineer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A one-page spec of the task, answer format and limits.
• A prompt draft in four parts, role, limits, reasoning and examples.
• At least three test cases, with the normal case, an odd case and a refusal case.
• A version note and a list of known limits.
• A list of gaps and checks to run before you rely on it.

How it does the job well

• Do not say a prompt "will" produce a result. Say what the tests are meant to show.
• If the task involves private data, advice that needs a professional, or actions on live systems, say what the prompt must refuse or hand off, and say a person must review it.
• Prepare drafts. Do not deploy a prompt or change a live system. Ask first.
• Never write a prompt without first defining the expected output format and success criteria
• Always version prompts — treat them like code (v1, v2, changelogs included)
• Test prompts against the actual model and temperature that will be used in production — behavior varies significantly
• Flag any prompt that relies on assumed knowledge the model may not have; ground it with context or examples instead

Mistakes it avoids

• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: You need an AI to do the same job each time, such as sort support emails, and the answers keep changing.

• Spec. The task, answer format, reader, common inputs and refusal rules, with gaps marked.
• Prompt draft. A role, limits with numbers, reasoning steps and example inputs with answers.
• Test cases. At least three cases, covering a normal input, an odd input and one that must be refused.
• What I could not test. The parts only you can check on your own AI and setting.
• Version note. The version, the date and the settings the prompt should be tested on.
• Known limits and checks. Where the prompt may fail and the steps to check before use.

How the work is checked

• The changed files build, and the relevant tests and linters pass. The commands and results are shown.
• Each change is explained in one line with its reason, and risky areas are named.
• Any check that could not run is reported as NOT_RUN with what it needs.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Write the spec. It sets the answer format, the three common inputs and the refusal rules before it writes any prompt.
• Draft the prompt. It writes role, limits, reasoning and examples, with each vague word like "short" turned into a number.
• Write the tests. It writes at least three cases, covering a normal input, an odd one and one that must fail safely.
• Mark what it cannot test. It says it has not run your prompt on your model, and names what you must test yourself.
• Fix one thing at a time. When you paste results back, it changes one part of the prompt, then asks you to run every test again.
• Hand off. It gives the final prompt, a version note, the settings tested and the known limits.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id engineering-prompt-engineer only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/engineering-prompt-engineer/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• The task in one or two sentences, and who or what reads the answer, such as a person or a program.
• The exact answer format you need, such as a table, a short email or fields in JSON.
• Three real inputs the AI will see most often (paste them with private details removed).
• What the AI must refuse, hand off or say when it does not know.
• The AI and setting it will run on, and a prompt you already have (optional).

Helpful context (optional)

• Ten real examples of the inputs, including messy ones.
• Two or three answers you would call good, as examples.
• Past wrong answers, with a note on what was wrong.

Tools and access

• The AI the prompt will run on: Run the test cases there, since the same prompt can behave differently on another AI or setting.
• A shared document or a code repository: Store each prompt version with its note and test cases so changes can be traced.

Functions and deliverables

• Prompt engineer: Describe the task and the answer format you need. Get a prompt spec, a first prompt draft and test cases to run before you rely on the prompt.
  • A one-page spec of the task, answer format and limits.
  • A prompt draft in four parts, role, limits, reasoning and examples.
  • At least three test cases, with the normal case, an odd case and a refusal case.
  • A version note and a list of known limits.
  • A list of gaps and checks to run before you rely on it.
  Source: Michael Sitarzewski and Agency Agents contributors.
• Guaranteed format generator: Say what shape each answer must take. Get Python code that makes a local AI model return valid JSON, a fixed choice or matching text.
  • A Pydantic class, JSON schema or choice list for your format
  • Python code that loads your model and generates against that shape
  • Setup steps for your model backend
  • A short sample check that shows the output type
  • Tips to keep it fast
  Source: Orchestra Research.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.

Tasks you can use it for

• You need an AI to do the same job each time, such as sort support emails, and the answers keep changing.
• You are building a customer-facing chatbot and need its rules, tone and limits written down.
• A prompt works on your test and then fails on real inputs.
• You are moving a prompt from one AI to another and need it checked and adapted.

Limits to check before starting

• You want to tidy one rough prompt for everyday chat. Use the AI prompt editor.
• Your prompt is already in use and you want it tested and fixed in rounds. Use the Prompt and skill improvement tester.
• You want to turn a job you repeat into a reusable skill file. Use the Reusable AI skill writer.
• You want to check a prompt for safety and bias risk. Use the Prompt safety and bias reviewer.
• You need a prompt tuned on a live system with its real model and settings. Test it there, with an engineer.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack from this page and add it to your AI as a skill. Or paste SKILL.md into a new chat, then paste references/source-agent.md after it.
• Describe the task, the answer format and your three real inputs. Say where the prompt will run.
• Answer the questions it asks once. Anything you cannot answer is marked MISSING in the spec.
• Run each test case on the AI and setting you will really use. Paste back what it answered.
• Fix one thing at a time. After each change, run all the test cases again.
• Keep the final prompt, its version note and its limits together, and store them where your team can find them.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/engineering-prompt-engineer for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.