AI Coding Assistant Workflow: A Safe Prompting and Code Review Guide

Overview

Use an AI coding assistant as a fast collaborator, not as an authority. It can produce plausible code that is incomplete, incompatible with your project, inefficient, insecure, or simply based on a misunderstanding of the requirement. The safest workflow keeps responsibility with the developer: define the task, limit the context, request a small change, inspect the result, run verification, and review the final diff.

Before using any assistant, check the tool's available privacy controls, data-handling settings, IDE permissions, repository access, and organization rules. These details differ between tools and may change over time. Avoid pasting secrets, private keys, access tokens, customer records, proprietary algorithms, or unannounced vulnerability details into a prompt. If code is sensitive, use an approved environment or replace real values with representative placeholders.

A useful prompt gives the assistant enough context to reason without asking it to guess. State the language and version, framework, relevant constraints, expected input and output, error behavior, and how the result will be tested. Ask for assumptions explicitly so you can challenge them.

We are using TypeScript with strict mode enabled. Refactor this function to avoid mutating its input.
Constraints:
- Preserve the public function signature.
- Keep the result stable for duplicate records.
- Return an empty array for missing input.
- Do not add dependencies.
First explain your approach and list edge cases. Then provide the patch and tests.

Checklist by scenario

Explaining unfamiliar code

• Provide only the relevant function, types, and nearby assumptions.
• Ask for a plain-language summary of control flow and side effects.
• Request a list of unknowns rather than accepting inferred behavior as fact.
• Compare the explanation with the actual callers, tests, and runtime configuration.

A useful prompt is: “Explain this module for a developer joining the project. Identify inputs, outputs, state changes, external calls, failure paths, and assumptions. Do not propose changes yet.” For frontend work, verify the explanation against browser behavior and existing debugging evidence. The frontend debugging checklist can help separate an AI hypothesis from an observed JavaScript, CSS, or browser issue.

Refactoring code

• Define what must remain unchanged, including public APIs and error behavior.
• Ask for one focused transformation rather than a broad rewrite.
• Request a before-and-after explanation and a list of behavior risks.
• Review the diff for accidental formatting, renamed exports, removed validation, and changed defaults.
• Run unit, integration, type, lint, and build checks appropriate to the repository.

Keep the first patch small enough to revert easily. If the assistant suggests a change to an API, cache policy, container, or deployment workflow, verify it against project documentation and operational requirements. Related checks may include the HTTP caching headers guide, Dockerfile best practices, or GitHub Actions deployment checklist.

Generating tests

• Describe the intended behavior, not only the current implementation.
• Ask for normal, boundary, invalid, and failure cases.
• Require tests for authorization, parsing, time zones, empty values, and duplicate data when relevant.
• Read every assertion to ensure it checks a meaningful outcome rather than merely matching the implementation.

Use AI to expand a test plan, then confirm the expected result independently. Generated tests can reproduce an existing bug or encode an incorrect assumption, particularly when requirements are implicit.

Writing documentation

• Give the assistant verified commands, configuration names, supported versions, and audience.
• Ask it to mark uncertain statements for confirmation.
• Test installation steps and code samples before publishing.
• Remove invented options, unsupported flags, and claims that are not backed by the project.

For data and API work, provide schemas without exposing real records. When reviewing JSON examples, use a formatter and validator to catch syntax and structure problems; the JSON formatter guide offers a practical verification path.

Tasks to limit or handle manually

What to double-check

Correctness and compatibility

Read the generated code line by line. Check types, nullability, asynchronous behavior, resource cleanup, error propagation, input validation, and compatibility with the project's runtime. Confirm that imports and APIs exist in the versions you actually use. A response can look idiomatic while relying on a library method or configuration option that is unavailable in your environment.

Security and privacy

Look for unsafe string interpolation, missing authorization checks, permissive CORS, unvalidated redirects, exposed error details, insecure deserialization, and logs containing sensitive values. Do not treat a statement such as “this is secure” as evidence. Test the security property directly and use established review processes for high-impact changes. For token troubleshooting, inspect only non-sensitive or deliberately redacted values; the JWT decoder guide explains why decoding a token is not the same as validating it.

Performance and operations

Ask how the proposal behaves with large inputs, slow dependencies, retries, concurrent requests, and partial failures. Check database queries for unnecessary scans, pagination errors, and unbounded results. Review logging, metrics, timeouts, and rollback behavior before merging operational changes.

Licensing and provenance

Understand your organization's rules for generated code and check the assistant's current terms before adopting it in a commercial or sensitive project. Keep normal code-review and dependency-review practices in place. Do not assume that generated output is automatically original, compatible with your license, or free of obligations.

Common mistakes

• Asking for an entire application at once. Large prompts hide assumptions and produce difficult-to-review output. Split work into interfaces, small functions, tests, and documentation.
• Sharing more context than necessary. Redact secrets and minimize proprietary material. Replace identifiers, URLs, and records with safe examples.
• Accepting the first answer. Ask the assistant to list assumptions, alternatives, edge cases, and reasons a proposal could fail.
• Reviewing only the added lines. Inspect deleted code, changed configuration, dependency updates, generated files, and migration order.
• Using tests as proof of correctness. Tests show that selected cases pass. They do not prove that the requirement was understood or that untested cases are safe.
• Letting an assistant act with excessive permissions. Limit repository, terminal, network, and deployment access to what the task needs, and require human approval for consequential actions.
• Skipping local project context. Start from the repository's conventions, scripts, architecture notes, and existing tests. A generic answer is rarely a drop-in solution.

When to revisit

Revisit this workflow whenever you adopt a new assistant, enable an IDE integration, change repository permissions, or move from personal experimentation to team use. Recheck privacy controls, retention settings, available model or agent capabilities, licensing terms, and organization policy rather than relying on an old setup.

Review the workflow before a new project, a major refactor, a seasonal planning cycle, or a change to deployment and incident procedures. Also update it after a near miss: an incorrect generated migration, leaked context, missed security issue, or review that accepted an untested assumption.

Use this short pre-merge routine each time:

1. Confirm the prompt contained no secrets or unnecessary private context.
2. Read the complete diff, including configuration and dependencies.
3. Write down the behavior the change is intended to preserve or add.
4. Run the relevant automated checks and inspect failures without asking the assistant to hide them.
5. Test important edge cases manually or with focused tests.
6. Have a human reviewer assess security, maintainability, and operational impact.
7. Record any tool-specific lesson in the team workflow so the next review starts with better constraints.

AI coding assistants are most useful when they shorten mechanical work while making reasoning more explicit. Keep the scope narrow, protect sensitive context, verify every meaningful claim, and make the final decision through the same engineering controls used for code written without AI.