An AI-assisted code review is most useful when it follows a repeatable process rather than a vague request to “check this code.” This guide shows how to estimate review effort and model usage, prepare a pre-PR and post-merge checklist, write focused code review prompts for LLMs, and choose an integration approach that fits your repository, privacy requirements, and maintenance budget.
Overview
LLMs can help reviewers identify overlooked edge cases, explain unfamiliar code, suggest tests, and highlight possible security or performance concerns. They do not replace ownership by the author or a qualified human reviewer. Their output can be incomplete, overly cautious, or incorrect, especially when the model lacks repository context.
A reliable AI code review workflow therefore has four parts:
- Scope the change: define what changed, which behavior is expected, and which risks matter.
- Provide controlled context: send the relevant diff, interfaces, tests, and conventions rather than an entire unfiltered repository.
- Request actionable findings: ask for file locations, reasoning, severity, and a suggested verification step.
- Validate before acting: reproduce the issue with tests, static analysis, a security check, or manual inspection.
This process also makes the workflow measurable. Instead of asking whether an AI tool is “worth it,” you can estimate review volume, tokens or requests, human verification time, and the cost of false positives. The result is a decision model that can be recalculated when models, pricing inputs, repository size, or review policy changes.
AI review works best alongside ordinary developer tools. For example, an LLM may explain a failing API contract, while an OpenAPI validator confirms the contract mechanically. It may suggest a missing security header, while a dedicated security test verifies the response. Related checks are covered in the OpenAPI validation and linting workflow and the HTTP security headers checklist.
How to estimate
Use a simple monthly estimate before adopting an AI code review tool or adding an automated review job to CI. The goal is not to predict usage perfectly; it is to expose the assumptions that drive the decision.
Monthly AI review cost = review requests × average request cost
If a provider bills by input and output usage, expand the formula:
Estimated usage cost = (input units × input rate) + (output units × output rate)
For a subscription or self-hosted option, include the fixed operating cost separately:
Total monthly cost = usage cost + subscription cost + infrastructure cost + human verification cost
Human verification deserves its own line. An AI finding that takes five minutes to confirm may still be useful; a stream of vague findings that takes longer to triage than a normal review may not be. A practical estimate is:
Human verification cost = findings reviewed × average minutes per finding ÷ 60 × internal hourly rate
Use your organization’s approved internal rate or leave this value as staff time if financial costing is inappropriate. Do not treat every generated comment as a defect. Track at least three categories:
- Confirmed issue: the finding is reproducible and should be fixed.
- Useful suggestion: not a defect, but a worthwhile improvement to tests, clarity, or maintainability.
- False positive or irrelevant comment: no action is required.
A basic outcome measure is confirmed findings per review hour. Compare it with the time required to inspect the same change without AI. This is a directional measure, not a universal benchmark. Review quality, codebase maturity, language, model choice, and prompt design all affect the result.
Inputs and assumptions
Record the following inputs in a small spreadsheet or repository document so the estimate can be reproduced:
- Pull requests reviewed per month.
- Average files, changed lines, or selected context per pull request.
- Number of review passes, such as general review, security review, and test review.
- Expected input and output usage, if the provider exposes those units.
- Current usage rates, subscription terms, or infrastructure assumptions.
- Average number of findings requiring human verification.
- Average minutes spent confirming each finding.
- Retention, access, and data-handling requirements approved for the project.
Keep prompts narrow. A useful request identifies the language, framework, changed behavior, and review goal. It also asks the model not to invent a problem. For example:
Review the following TypeScript pull request for authorization flaws.
Context: the endpoint updates a resource owned by the authenticated user.
Return only findings that are supported by the diff or stated context.
For each finding, include:
- file and approximate location
- risk and affected behavior
- why the current code is vulnerable
- a minimal remediation
- a test that could verify the fix
If no issue is supported, say “No supported finding.”
Diff:
[PASTE REVIEWED DIFF HERE]Reuse the structure for other goals:
- Tests: “Identify behavior branches not covered by the existing tests. Propose test cases with inputs, expected results, and the reason each case matters.”
- Performance: “Look for avoidable repeated work, unbounded data access, blocking operations, and inefficient queries. Separate confirmed concerns from hypotheses and state what measurement would verify each one.”
- Style and maintainability: “Check consistency with the supplied project conventions. Do not suggest broad rewrites. Report only changes that improve correctness, readability, or maintenance.”
- Dependency changes: “Review this dependency update for breaking API assumptions, lockfile changes, permissions, and test gaps. Distinguish information that requires checking from an actual defect.”
Protect the review boundary. Remove secrets, tokens, private customer data, and unnecessary production records. Prefer a sanitized diff and representative fixtures. A JWT decoder can help inspect token structure during debugging, but a decoded token should not be treated as verified; see the JWT decoder versus verifier guide for that distinction. Likewise, use local or approved tooling when repository content cannot be sent to an external service.
Integration choice is a trade-off rather than a ranking:
| Approach | Useful when | Questions to confirm |
|---|---|---|
| IDE assistant such as Copilot or Codeium | A developer wants immediate, interactive feedback while editing. | What context is shared, where are review records stored, and how can suggestions be independently tested? |
| GitHub Actions plus an LLM API | The team wants a repeatable pull-request gate or advisory comment. | How are secrets managed, how are diffs limited, and what happens when the service fails? |
| Local model | Repository control, offline operation, or data boundaries are primary concerns. | Can the hardware run the chosen model consistently, and who maintains the runtime and model updates? |
| SaaS review tool | The team prefers a packaged workflow with configuration and reporting. | Which languages and providers are supported, what controls exist, and how are pricing inputs calculated? |
Worked examples
Example 1: advisory pull-request reviews. Assume a team has 80 pull requests each month and runs one AI review per pull request. If each review uses an estimated 0.6 input units and 0.15 output units, monthly usage is 48 input units and 12 output units. Substitute the applicable input and output rates into the usage formula. If the resulting comments require 160 total minutes of human verification, that is 2.67 staff-hours before any fixes are made. The team can compare that time with the number of confirmed findings and useful suggestions recorded during the same period.
Example 2: multiple review passes. A smaller team reviews 40 pull requests monthly, but runs three focused passes: general correctness, security, and tests. That is 120 review requests before retries. If 10 percent of requests are repeated because of CI failures or insufficient context, the planning volume is 132 requests. This does not establish a price; it gives the team a transparent quantity to multiply by the current rate or resource estimate.
Example 3: comparing options. Suppose a SaaS tool has a fixed monthly charge, an API workflow has variable usage plus maintenance time, and a local model has infrastructure and administration time. Put all three in the same table using identical review volume, retention requirements, and verification assumptions. Then add qualitative factors such as failure handling, auditability, repository context, and ease of disabling the check. The cheapest numerical estimate may not be the lowest operational burden.
For every approach, run a small pilot with representative pull requests. Label findings before changing the prompt: confirmed issue, useful suggestion, false positive, or out of scope. This prevents a prompt change from appearing successful merely because it produces more comments.
Use a checklist at each gate:
- Pre-PR: tests pass, secrets and sensitive fixtures are removed, the diff is focused, and the author states expected behavior and known risks.
- In-PR: AI findings include locations and reasoning, humans reproduce important claims, and automated checks remain authoritative for formatting, types, tests, and security rules.
- Post-merge: monitor regressions, record accepted and rejected findings, and update prompts when a recurring false positive or missed case is understood.
When to recalculate
Recalculate the estimate when pricing inputs change, a model or integration is replaced, review volume shifts, or the average diff becomes substantially larger or smaller. Also revisit it when the team changes its security policy, data-retention requirements, CI retry behavior, or human approval rules.
Review the workflow after a significant incident, a missed defect, or a pattern of noisy comments. A model update can alter output length, usefulness, and false-positive behavior even when the prompt stays the same. Record the model or tool version, prompt revision, context supplied, and evaluation period so results remain comparable.
Start with one review goal and one repository. Establish a baseline, run the estimate, and pilot the workflow for a defined set of pull requests. Keep automated tests and specialist scanners in their normal roles, use the LLM for explanation and targeted analysis, and require human validation for decisions that affect security, data access, or production behavior. Recalculate the inputs after the pilot, then decide whether to expand, narrow, or retire the AI review step.
For adjacent workflow improvements, see the AI-assisted coding workflow guide, the npm dependency upgrade checklist, and the web accessibility testing workflow.
