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A dependable semantic code-review answer is a versioned, machine-readable contract—not a paragraph of AI-generated comments. Emit one overall verdict and explanation, then findings that include an actionable description, model confidence, exact file and line ranges, and citations joined to stable evidence records. Keep retrieval evidence separate from prose so every claim can be audited, permissions can be enforced, and a reviewer can verify the cited code before merging.
This design works for bots that combine lexical search, semantic search, extractive answers, and generated explanations. The key is to preserve provenance at every boundary: query, repository scope, retrieved document, claim, citation span, generated finding, and final rendered response.
What should a structured code-review answer contain?
Start with a small top-level contract that is easy for both a user interface and a validator to consume. The overall result tells the reviewer whether the change is acceptable; the findings explain what must be checked or fixed.
| Field | Purpose | Required shape |
|---|---|---|
overall_correctness |
Machine-readable verdict for the reviewed change. | Boolean or an explicitly documented enum such as pass, needs_changes, or blocked. |
overall_explanation |
Short rationale for the verdict. | String grounded in cited claims. |
overall_confidence_score |
Confidence in the verdict, separate from evidence quality. | Numeric score with a documented range, usually 0 to 1. |
findings |
Actionable review comments. | Array; each item has a title, body, confidence score, code location, and citation references. |
The structured review example in the OpenAI Cookbook requires the first four concepts and requires each finding to identify a title, body, confidence score, and code location. Treat that shape as a contract: a renderer can display it, a CI job can gate on it, and a later reviewer can inspect it without parsing prose.
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Use exact locations, not approximate references
Every finding should carry an absolute path, a repository-relative path, and inclusive start and end lines. Keep line numbers as integers, not text embedded in a sentence. If a finding spans a function, report the smallest range that demonstrates the issue; if the evidence is in another file, cite that file separately rather than stretching the review location.
Make each finding actionable
A useful body states the observed behavior, why it matters, and a concrete remedy or verification step. “This may fail” is incomplete. Explain the input or execution path that triggers the failure, the expected behavior, and what the author should change or test.
What JSON schema should a code-review bot emit?
Use a stable identifier, explicit version, reusable definitions, and strict location types. The following compact contract can be expanded without breaking consumers when new optional fields are added.
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Separate required facts from optional enhancements
Keep the core contract small. Add optional fields for severity, category, suggested patch, test commands, review status, or suppression reason only after consumers can ignore unknown properties safely. If you need strict compatibility, publish a new major schema identifier when a required field changes type or semantics.
How should citations be modeled for review findings?
Represent evidence independently from generated prose. Decompose an explanation into atomic claims, then attach one or more citation IDs to each claim. A citation ID points into a source registry containing the exact document, chunk, and snippet used to support the claim.
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Use a source registry
A practical registry record contains:
- source_id: an immutable identifier used by claims and findings.
- document_key: the repository document, commit, or indexed record key.
- document_title_or_path: the human-readable file or documentation name.
- chunk_id: the indexed segment that was retrieved.
- snippet: the exact text or code span returned by retrieval.
- retrieval_timestamp: when the evidence was fetched.
- repository_or_tenant_scope: the permission boundary in force at retrieval time.
- evidence_confidence: confidence that the retrieved material supports the claim.
- validation_status: whether the citation still joins to an accessible, unchanged source.
Keep data-call provenance and detected entities in separate arrays when your system needs them. This answer/claims/sources/dataCalls/entities separation, documented by eli.ai, allows a renderer to validate joins and gives a later reviewer the exact evidence behind each sentence.
Store spans, not just document names
A file name alone cannot prove a claim. Store a line range, character offsets, or a verbatim snippet for every code citation. If the source is documentation, retain the document key and the returned text. If the source is code, preserve the commit or index version so a citation does not silently point at different content after a force-push.
How can semantic search return evidence for a review answer?
Use lexical and semantic retrieval for different jobs, then retain the results from both. Lexical search is strongest for exact identifiers, function names, error strings, and configuration keys. Semantic search is strongest for conceptual intent, such as “where is authorization checked before this operation?”
| Retrieval mode | Best use | Typical control | Primary risk |
|---|---|---|---|
| Lexical | Exact names, literals, symbols, and stack-trace text. | Token, phrase, or regular-expression query. | Misses relevant code expressed with different names. |
| Semantic | Conceptual relationships and natural-language questions. | Embedding similarity with repository and directory filters. | Returns plausible but irrelevant matches; results are probabilistic. |
| Hybrid | Reviews requiring both exact and conceptual evidence. | Merge, rerank, and deduplicate lexical and vector results. | More moving parts and harder confidence calibration. |
GitLab describes semantic code search as converting repository code into vector embeddings, storing them in a vector database, and comparing a natural-language query embedding with code embeddings. Its API exposes a natural-language q plus directory_path, knn, and limit controls. Preserve those request parameters with the citations so another reviewer knows what was searched.
Enforce repository permissions before generation
Atlassian documents that semantic results are scoped to the user’s existing repository permissions. Apply that scope during retrieval, not after the model has seen results. A generated answer must never reveal a file, symbol, or snippet from a repository or directory the requesting identity cannot access.
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For extractive semantic answers, Azure AI Search requires a question-shaped query, semantic query parameters, and configured content fields. A semantic answer is verbatim content recognized in indexed documents, not newly composed text. Store its document key, returned text, and confidence beside the citation record; do not present a generated paraphrase as if it were an extract.
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How do I generate a finding without hiding weak evidence?
Run generation only after retrieval and claim assembly. Keep model confidence separate from evidence confidence: a model can sound certain while its retrieved support is weak or out of date.
- Capture the review scope. Record repository, commit or merge request, changed files, identity, and permission scope.
- Retrieve candidates. Run exact searches for symbols and strings, then semantic searches for behavioral questions. Store query parameters and ranking scores.
- Normalize evidence. Resolve each result to a stable document key, line range, snippet, and source ID. Mark inaccessible or stale records invalid.
- Decompose claims. Write one atomic statement per observed behavior, impact, or recommendation. Attach only the source IDs that support that statement.
- Generate the finding. Require a title, actionable body, confidence score, exact code location, and citation IDs. Instruct the model to say when evidence is insufficient.
- Validate joins. Check that every citation ID exists, every location is inside the reviewed revision, and every claim has supporting evidence.
- Render and inspect. Verify the final provider response, because structured-output fields do not inherently carry citations.
Atlassian warns that AI responses are probabilistic and should be verified before use, particularly for incidents or security decisions. Treat a high model score as a prioritization signal, not approval to merge.
How should confidence scores be interpreted?
Publish at least two confidence values. model_confidence describes how certain the generator is about its interpretation; evidence_confidence describes how directly the retrieved sources support it. A third optional value, location_confidence, records whether the cited lines precisely contain the behavior.
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- High model, low evidence: show a warning and request more retrieval; never state the conclusion as fact.
- Low model, high evidence: expose the cited evidence and ask a reviewer to interpret it.
- Low model, low evidence: omit the finding or label it an unverified lead.
Do not invent universal thresholds. Calibrate them against your repository, language mix, and review consequences, then version the policy alongside the schema.
What makes a citation schema maintainable over time?
Version the contract and the evidence independently. A schema version describes field semantics; an index or repository revision describes the material being cited. Store both so a response can be replayed and audited.
Prefer reusable definitions
Define code_location, citation, and claim once, then reference them from findings and summaries. Reusable definitions prevent one endpoint from treating line numbers as strings while another treats them as integers.
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Plan for changed code
When a commit changes, revalidate every citation. A finding whose lines moved should be marked stale, not silently remapped by line number alone. If the exact snippet still exists at a new location, record the new revision and the remapping method for auditability.
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For a public question-and-answer surface, Google’s QAPage model separates a question from accepted or suggested answers and supports answer text, authorship, publication or modification dates, and direct answer URLs. That model is useful for presentation metadata, but internal code-review provenance still belongs in your source registry and claim joins.
What changes when the output provider cannot carry citations?
Do not assume that a valid structured response automatically preserves citations. Pydantic AI documents that structured output fields do not inherently carry document citations and that behavior varies by provider and output mode.
- Keep citation IDs in explicit response fields whenever the provider permits them.
- If citations must be inline, use a documented text convention that your renderer parses and validates.
- Persist the raw provider response alongside the parsed object.
- Run a post-processing check that every displayed citation resolves to a source record.
- Fail closed when a citation is dropped, malformed, or points outside the caller’s scope.
Inspect the final response, not only the model’s intermediate message. A provider adapter that strips annotations can turn a well-supported finding into an unauditable comment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a review bot handle missing or conflicting evidence?
Make uncertainty a first-class result instead of forcing a verdict. If lexical and semantic retrieval disagree, show the conflict in the finding’s evidence metadata and ask for a targeted check.
When no supporting citation is found
Return no actionable finding, or return a clearly labeled unverified observation without merge-blocking severity. Include the query and scope used so a reviewer can refine retrieval.
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When sources conflict
Prefer the source from the reviewed revision for code behavior. For documentation or policy, retain both records, identify their versions, and explain the discrepancy. Never merge contradictory snippets into one synthetic quote.
When a location is invalid
Reject the finding during validation. Ask the generator to regenerate against the current revision rather than allowing a comment to land on an unrelated line.
Which schema design is best for reviewer verification?
A design that maximizes traceability usually requires more storage than a free-form response, but it reduces the time needed to confirm a claim. Compare designs on the dimensions that affect real review work.
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| Design | Citation traceability | Line precision | Versioning | Permission preservation | Reviewer effort |
|---|---|---|---|---|---|
| Free-form comments | Low; evidence is implicit. | Often approximate. | Ambiguous. | Depends on surrounding system. | High. |
| Findings with embedded snippets | Medium; snippets can be inspected. | Good if ranges are typed. | Requires revision metadata. | Must be enforced at retrieval. | Medium. |
| Findings plus claim and source registries | High; each claim joins to stable IDs. | Exact paths and lines. | Explicit schema and source revisions. | Scope can be audited per source. | Lowest when validation is automated. |
| Extractive answer records | High for quoted text. | Depends on indexed offsets. | Strong when document keys are retained. | Inherited from search scope. | Low for quote checking, higher for interpretation. |
For production review automation, the registry-based design is the strongest foundation. It supports generated explanations while preserving the original evidence and lets you replace a model or search provider without rewriting the review history.
What is the practical contract for a trustworthy semantic review?
Emit a verdict, but make every consequential sentence inspectable. Stable schema identifiers, typed paths and line ranges, atomic claims, source IDs, retrieval scope, separate confidence values, and provider-aware validation turn semantic search from a plausible-text generator into an auditable review system. Semantic retrieval can broaden discovery, while lexical retrieval anchors exact symbols; neither removes the need for permission checks and human verification.
Frequently Asked Questions
Should citation snippets be stored in the review database or fetched later?
Store the returned snippet with its document key, revision, and retrieval timestamp, then optionally refresh it for display. A later fetch alone can show different code after a commit or reindex and makes historical audits unreliable.
Can a review finding cite more than one file?
Yes. Keep the finding’s primary code location where the issue appears, and attach additional source IDs for related definitions, tests, or configuration. This preserves one clear review anchor without losing cross-file evidence.
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What should happen when a semantic result comes from a private directory?
Filter it before generation using the caller’s repository permissions. If the result is later found to be out of scope, invalidate the citation and regenerate the answer rather than redacting the snippet after the model has processed it.
Is an extractive semantic answer safe to paraphrase?
Paraphrasing is allowed only when the response labels it as generated explanation and retains the original extracted text and document key. Otherwise readers may mistake a model’s wording for verbatim indexed content.
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