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This 90-minute workshop helps engineers understand what an AI code reviewer can see, practice shaping that context, and verify its findings against the diff and repository evidence. The key idea: a review prompt is only one part of the model’s working context, and a larger context window does not guarantee a better review.
What “context” means in an AI code review
Context is the working set available to a model for a particular invocation. Depending on the product, it may include the current request, standing instructions, conversation history, repository or project files, and earlier tool calls and their outputs. The exact construction differs by product.
Anthropic says a Claude Code turn includes the conversation so far, project context such as CLAUDE.md and files Claude has read, and the latest prompt. OpenAI describes an agent loop in which tool outputs can be appended to the prompt as the conversation grows. That accumulated material can eventually exhaust the context window.
A context window is a capacity limit, not a score for review quality. OpenAI notes that capacity includes both input and output tokens. Nor does fitting more material into a window mean the model will give every item equal attention. The practical goal is to provide relevant evidence and instructions, while avoiding stale or conflicting material.
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90-minute workshop plan
The schedule below is a workshop proposal, not a tested or published curriculum. It can be run with a real pull request, a prepared example, or a simulated review.
| Time | Activity | Participant outcome |
|---|---|---|
| 0–10 minutes | Establish the context model | Identify likely inputs: request, standing instructions, prior conversation, files and diff, tool outputs, and response capacity. Ask what participants think the reviewer can actually see. |
| 10–25 minutes | Inventory context | Label items in a sample pull request and fictional transcript as necessary, useful, stale, or conflicting. These labels are a teaching device, not a universal measured taxonomy. |
| 25–45 minutes | Shape the review request | Write a focused request stating the goal, changed areas, relevant paths or conventions, and expectations for evidence and uncertainty. |
| 45–65 minutes | Run or simulate a review | Compare findings with the diff and repository facts; mark each supported, unsupported, duplicate, or a missed concern. |
| 65–80 minutes | Discuss scope and operating constraints | Compare context coverage, excluded files, instruction controls, operational effort, cost, and human control for the workflow being considered. |
| 80–90 minutes | Decide what to retain | Move only recurring, durable corrections into repository guidance; keep temporary review details in the task request. |
How to inventory and curate context
1. Establish what the reviewer can see
Before asking for a review, list the actual change, the files and conventions relevant to it, and any earlier discussion that may no longer apply. Do not assume that every code-review product receives the same inputs. Confirm whether it can inspect repository files, only the diff, or additional issue and project context.
2. Separate durable guidance from task details
Put recurring rules in the repository’s instruction mechanism when they apply across tasks. Keep a particular pull request’s objective, suspected failure modes, and review priorities in its request. GitHub documents several distinct mechanisms for its Copilot workflows, including repository-wide instructions, path-specific instructions, shared AGENTS.md files, and task-specific skills.
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3. Remove irrelevant accumulation
When changing to an unrelated task, start a fresh session if the product supports it. When continuing a long task, summarize the facts that must carry forward and remove irrelevant history where possible. For Claude Code specifically, Anthropic documents /clear for switching tasks and /compact for continuing a long one. These are product-specific commands; do not assume equivalent commands or behavior in other tools.
When asking a coding agent to inspect material, referencing relevant file paths can be more selective than pasting whole files into a prompt. Anthropic’s Claude Code guidance distinguishes between pointing to files and injecting their contents.
4. Ask for evidence, then verify it
For the exercise, ask the reviewer to identify the affected behavior, point to relevant changed lines or files, explain a plausible failure scenario, and state uncertainty. These are useful review-request elements, not a guarantee of correctness.
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- Trace each finding to the changed code and the behavior it claims is affected.
- Check the relevant tests and repository conventions.
- Mark findings that are unsupported, duplicate, or outside the requested scope.
- Record important concerns the reviewer missed, without treating the exercise as a benchmark unless it was run and documented that way.
How to compare AI code-review workflows
There is no universal winner. In the workshop, compare the actual options against the same change and use criteria that matter to your team.
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| Criterion | Questions to ask |
|---|---|
| Context coverage | Can the reviewer inspect the full repository, selected files, linked issue context, or only the diff? |
| Scope transparency | Can you determine which files were reviewed and which were excluded? |
| Instruction control | Can you provide repository-wide, path-specific, and task-specific conventions? |
| Finding quality | Are findings specific, evidence-linked, actionable, and appropriately uncertain? Evaluate this locally; the sources cited here do not establish a neutral comparative accuracy benchmark. |
| Operations | What configuration, runner availability, review-effort settings, and usage budgets are required? |
| Human control | Who requests the review, how are suggestions applied, and what verification remains with the team? |
For example, GitHub’s Copilot code-review documentation describes full-project context gathering and lists exclusions including dependency-management files, log files, and SVG files. It also documents repository and path-specific instructions, Actions runner considerations, review-effort settings, and a public-preview capability to pass suggestions to Copilot cloud agent to create a pull request with suggested fixes. These are GitHub-specific details and may change; check the current documentation before planning around them.
GitHub’s documentation estimates AI-credit consumption of $0.05–$1 USD per review at its Lite effort and $0.25–$5 USD at Balanced effort. These are vendor estimates, not fixed prices: actual usage generally rises with pull-request size and repository instructions, the ranges can change as models evolve, and the estimates exclude GitHub Actions minutes. Verify the current figures before using them in a budget.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the workshop should leave participants with
- A shared understanding that the prompt is only one part of the reviewer’s context.
- A repeatable habit of checking context for relevance, staleness, and conflicting instructions before a review.
- A focused request that names the review goal, relevant areas, and expectations for evidence.
- A verification routine that tests findings against the diff, expected behavior, tests, and repository conventions.
- A distinction between durable repository guidance and temporary task-specific details.
Do not use an AI review as a substitute for human verification. The workshop is designed to make the evidence behind each finding visible and testable, not to presume that a model has found every defect.
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