AI Workflow Checklist for Developers

A reliable AI workflow is not a single prompt. It is a repeatable loop: define the task, provide safe context, ask for checkable output, verify the result, and keep a record of the evidence.

1. Define the task

Write the task in one sentence before opening the AI tool.

Good task shapes:

• Explain this error and list likely causes.
• Draft tests for this behavior.
• Compare two implementation options.
• Refactor this function without changing its public contract.
• Summarize these notes into requirements.

Avoid broad ownership prompts such as Build the whole feature unless an agent has clear files, constraints, and checks.

2. Prepare safe context

Include only the context needed for the task:

• Relevant code, logs, docs, or requirements.
• Expected behavior and actual behavior.
• Constraints that must not change.
• Existing commands, tests, or examples.

Remove secrets, private customer data, internal strategy, and source code that should not leave your environment.

3. Ask for checkable output

Make the answer easy to review:

Return:
1. Short diagnosis.
2. Recommended change.
3. Risks and assumptions.
4. Verification steps.

For structured work, ask for JSON, Markdown tables, SQL, test cases, or a patch-sized explanation. The more concrete the format, the easier it is to inspect.

4. Verify before trusting

Use the right evidence:

• Code: run tests, type checks, linting, and manual review.
• API or library details: check official docs.
• Security-sensitive work: use trusted references and team policy.
• Content: check source material, tone, claims, and permissions.
• Data transformations: compare sample input and output.

If the answer cannot be verified, treat it as an idea, not a result.

5. Record what happened

For important work, leave a small note:

AI assisted with: [task]
Verified by: [docs, tests, command, review]
Remaining risk: [known limitation]

This helps future reviewers understand where human judgment was applied.

Common failure patterns

• The prompt hides the most important constraint.
• The assistant changes behavior while claiming it only refactored.
• The answer cites precise facts without sources.
• The task includes sensitive data that should have been redacted.
• The verification step is vague, missing, or impossible to run.

Final takeaway

Use AI as a fast collaborator inside an engineering workflow. Keep ownership of context, privacy, review, and evidence.