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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →An effective AI code-review workshop teaches participants to manage the model’s working context, not just to write a clever prompt. In 90 minutes, attendees can inventory what the reviewer may see, curate a review request, inspect findings against the diff and repository evidence, and decide what guidance belongs in durable project instructions.
What “context” means in an AI code review
Context is the working set available to a model during an invocation. Depending on the product, it can include the current request, standing instructions, prior conversation, repository files, diffs, and outputs from earlier tool calls. A review prompt is only one part of that set.
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Context-window capacity is a limit, not a measure of attention or quality. OpenAI explains that a model’s context window is the maximum number of tokens it can use for one inference call, and that input and output tokens both count toward capacity (OpenAI, January 23, 2026). Claude Code’s context for a turn includes the conversation so far, project context such as CLAUDE.md and files it has read, and the latest prompt (Anthropic Claude Code documentation). Implementations differ, so participants should establish what their chosen reviewer actually receives.
Long sessions can accumulate material: OpenAI describes tool outputs being appended to the prompt and conversation history growing across turns. More context is not automatically better if it contains stale, unrelated, or conflicting information. A useful workshop question is therefore not just “What should I ask?” but “What information is in scope, and what should be removed or made explicit?”
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A 90-minute workshop plan
The schedule below is a practical workshop proposal, not a tested or benchmarked curriculum. It works with a live AI review product or a simulated review, provided participants have a sample pull request and enough repository context to inspect claims.
| Time | Activity | Participant outcome |
|---|---|---|
| 0–10 minutes | Establish the context mental model | Identify what the agent may receive beyond the latest request. |
| 10–25 minutes | Inventory context | Separate useful material from stale, unrelated, or conflicting material. |
| 25–45 minutes | Curate a review request | Write a concise request that identifies review goals, relevant areas, conventions, and evidence expectations. |
| 45–65 minutes | Run or simulate the review | Assess findings against the actual changes and repository facts. |
| 65–80 minutes | Discuss scope and constraints | Compare workflow options using context coverage, exclusions, controls, effort, and cost. |
| 80–90 minutes | Decide what to retain | Move durable corrections into appropriate project guidance and leave temporary details with the task. |
0–10 minutes: Establish the mental model
Draw or display a simple inventory: the user’s request, standing instructions, conversation history, files and diffs, tool outputs, and the capacity available for the response. Ask participants what they believe the reviewer can see before explaining that the answer depends on the product and its configuration.
10–25 minutes: Inventory context
Give small groups a sample pull request and a fictional transcript containing relevant instructions alongside unrelated or outdated discussion. Ask them to label each item as necessary, useful, stale, or conflicting. These labels are a teaching device, not a universal or measured taxonomy. The point is to make hidden assumptions about context visible.
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25–45 minutes: Curate the review request
Have each participant write a request that states the review goal, names changed areas, points to relevant files or paths, gives applicable conventions, and asks for evidence and uncertainty to be made explicit. When the agent can read files selectively, referring to paths can be more useful than pasting whole files; Anthropic’s Claude Code guidance distinguishes those approaches (Anthropic Claude Code documentation).
45–65 minutes: Run or simulate the review
Participants inspect each finding against the diff and relevant repository facts. Ask them to classify it as supported, unsupported, duplicate, or a missed concern, and to record the evidence behind the classification. This is an exercise in disciplined review, not a product benchmark unless the group actually runs and records a defined evaluation.
65–80 minutes: Discuss scope and constraints
Use the workflow dimensions in the next section to compare real options. Ask what the system inspected, what it could not inspect, what configuration it needs, who initiates a review, and what costs or operational work the team must account for.
80–90 minutes: Decide what to retain
Ask participants to extract only recurring, durable corrections for repository guidance. Keep details specific to one pull request in its task request. Anthropic advises keeping always-on project guidance lean; GitHub documents separate roles for repository-wide instructions, path-specific instructions, shared AGENTS.md, and task-specific skills.
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Start with the change and its relevant evidence
Identify what changed, which behavior the change is meant to affect, and which files or conventions are needed to understand it. Include the pull request’s purpose and point to relevant paths when the product can inspect them. Avoid treating a large transcript or repository dump as inherently useful: the goal is a relevant working set, not maximum volume.
Separate standing rules from task-specific details
Put durable conventions—such as recurring architecture or testing expectations—in the project’s supported instruction mechanism. Keep temporary requirements, edge cases, and review questions in the request for that task. GitHub’s documentation describes distinct mechanisms for repository-wide Copilot instructions, path-specific instructions, shared AGENTS.md, and task-specific skills (GitHub Copilot code review documentation).
Persistent instructions also consume context and can conflict. Anthropic reports that it removed over 80% of Claude Code’s system prompt for the models named in its 2026 article, with no measurable loss on its own coding evaluations. That is an Anthropic-reported internal result about its coding evaluations—not an independent code-review benchmark or a general rule that shorter prompts are always better (Anthropic, July 24, 2026).
Trim accumulated history when the task changes
For a new, unrelated task, use a clean session when the product supports one. When continuing a long task, preserve a concise summary of what must carry forward and discard irrelevant history where possible. Anthropic documents /clear for switching tasks and /compact for continuing long work in Claude Code (Anthropic Claude Code documentation). These are Claude Code examples, not universal commands. OpenAI describes automatic compaction in Codex (OpenAI, January 23, 2026); the exact behavior and controls are product-specific.
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Ask for evidence, then verify it yourself
A workshop request can ask the reviewer to name the affected behavior, identify relevant changed lines or files, explain a plausible failure scenario, and state uncertainty. These instructions can make findings easier to evaluate, but they do not guarantee correctness. Participants should trace every claim to the actual code and verify it against expected behavior, tests, and applicable repository conventions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to compare when choosing a review workflow
There is no universal winner. Compare the workflow on dimensions that affect the review your team needs, and establish what the product actually inspected before judging its findings.
| Dimension | Questions to ask | Documented example |
|---|---|---|
| Context coverage | Can the system inspect the full repository, selected files, linked issue context, or only the diff? | GitHub documents full-project context gathering for its agentic code-review capability. |
| Scope transparency | Which files or file types are excluded, and can reviewers tell what was skipped? | GitHub lists dependency-management files, log files, and SVG files among exclusions for its feature. |
| Instruction control | Can teams apply repository-wide, path-specific, or task-specific conventions? | GitHub documents these instruction options, including shared AGENTS.md and task-specific skills. |
| Finding quality | Are findings specific, evidence-linked, actionable, and appropriately uncertain? | Assess this locally in the workshop; the cited sources do not establish a neutral comparative code-review benchmark. |
| Operational constraints | What configuration, runner availability, review-effort setting, and usage budget are required? | GitHub documents runner use and review-effort settings for its feature. |
| Human control | Who requests the review, how are suggestions applied, and what verification remains with the team? | GitHub documents an option to pass suggestions to Copilot cloud agent to create a pull request with suggested fixes; its documentation labels this capability public preview and subject to change. |
For GitHub’s documented feature, the current guidance accessed October 7, 2026 estimates AI-credit consumption of $0.05–$1 USD per review at “Lite” effort and $0.25–$5 USD per review at “Balanced” effort. GitHub says actual usage generally rises with pull-request size and repository instructions, that the estimates can change as models evolve, and that they exclude GitHub Actions minutes. These are vendor estimates for that product, not general AI-review costs; check the GitHub documentation for current details.
How to judge an AI review finding
Use the finding as a claim to test, rather than as a verdict. For each issue raised, ask:
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- Does it concern code changed in this pull request, or a relevant interaction with existing code?
- Can the claimed behavior or failure scenario be traced to the cited code and repository evidence?
- Does a test, established convention, or explicit requirement support or contradict the finding?
- Is the finding actionable and distinct, or is it unsupported, duplicated, or based on a misunderstanding?
- What important concern might the review have missed?
Neither the cited vendor documentation nor the workshop proposal establishes a neutral statistic for AI code-review accuracy or defect detection across products. Treat quality as something to evaluate against your own review criteria, not as a performance claim inferred from a vendor’s description of its feature.
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