RAG for Code Debugging AI: Turning Logs and Diffs into Instant Fixes
Software teams don’t need another chatbot that explains what a NullPointerException is. They need fixes. Concretely: given a stack trace, a failing test, and the last few months of diffs and PR discussions, the system should propose a small, safe patch and run a targeted test plan. Retrieval-augmented generation (RAG) is the correct backbone for this, but only if we treat engineering artifacts as first-class signals and design for real-world constraints like private code, tenant isolation, latency, and regression risk.
This article lays out a practical, opinionated blueprint for building a private RAG stack that turns logs and diffs into instant fixes. We will focus on:
- Data: ingesting stack traces, test failures, and PR history into a robust schema
- Retrieval: hybrid and structure-aware search tailored to code and CI noise
- Generation: prompts, patch formats, and execution sandboxes
- Guardrails: policy, evaluation, and risk scoring to keep changes safe
- Operations: cost, latency budgets, and observability in production
If you already have CI logs, a code host, and a vector database, you can get a first version working in a week. The difference between a demo and a dependable system is in the details below.
Why RAG for Code Debugging is different
Generic RAG retrieves public webpages and synthesizes a prose answer. Code debugging RAG operates on:
- Highly structured artifacts: stack traces, JUnit XML, diff hunks, code symbols, PR review threads
- Strong priors: the fix usually localizes near the top stack frame, the changed file in the last PR touching that module, or a config toggle mentioned in the error
- Hard correctness constraints: any change must compile, pass tests, and respect coding standards
- Tenant privacy and organizational context: on-prem code, internal libraries, and tribal knowledge in PR comments
That means your RAG system must be:
- Private by default: data never leaves your trust boundary
- Schema- and relation-aware: not just bag-of-words vectors
- Feedback-driven: test outcomes, revert signals, and developer votes must update ranking and generation behaviors
System overview
A minimal, pragmatic architecture:
- Ingest
- CI logs (build output, JUnit XML, failing tests)
- Runtime errors (Sentry-like events, stack traces)
- Git history (commits, PRs, diffs, review comments)
- Code symbols (function signatures, types) and dependency edges (call graph when feasible)
- Normalize
- Canonical schemas per artifact with cross-links (e.g., a stack trace frame links to a code symbol and to PRs that last modified that file)
- Index
- Vector: embeddings for code, diffs, and error messages
- Lexical: BM25 for exact error tokens and identifiers
- Symbol index: function and class lookup
- Graph edges for temporal and structural proximity
- Retrieve
- Build a multi-channel query from the latest failure: top stack frames, error type, failing test name, repo, branch, and recent PRs touching those files
- Hybrid search with filters (repo, branch, visibility)
- Rerank with a cross-encoder that understands code and diffs
- Generate
- Constrained patch proposal in a sandboxed workspace
- Targeted test execution and lints
- Produce a PR or commit message with references back to context
- Guardrail
- Diff risk scoring, policy checks, static analysis gates
- Strict fail-safe: comment with analysis if patch risk exceeds thresholds or tests cannot be reproduced deterministically
- Observe and learn
- Capture telemetry on retrieval quality, patch outcomes, reverts
- Continuous evals on a corpus of known failures and synthetic mutants
Data: schemas that survive real failures
Most debugging context arrives messy. A reliable RAG system imposes a structure that can be indexed and joined. Below is a recommended document schema set and link strategy.
Core document types
- stack_trace
- fields: tenant_id, repo_id, commit_sha, branch, service, env, error_type, error_message, frames, timestamp, incident_id, ci_job_id
- frames: list of { file_path, function, line, module, language, in_repo, symbol_id }
- test_failure
- fields: tenant_id, repo_id, commit_sha, branch, test_suite, test_name, classname, failure_message, failure_type, stack_trace_id, artifacts_path, junit_xml_path, duration_ms, timestamp, ci_job_id
- pr
- fields: tenant_id, repo_id, pr_number, title, description, authors, reviewers, labels, created_at, merged_at, merge_commit_sha
- diff_hunk
- fields: tenant_id, repo_id, pr_number, file_path, hunk_id, before_range, after_range, patch_text, summary, symbols_touched, risk_score_baseline
- code_symbol
- fields: tenant_id, repo_id, path, language, symbol_name, kind (function, class, method), signature, start_line, end_line, last_modified_commit, code_excerpt
- ci_job
- fields: tenant_id, repo_id, provider, job_id, workflow_name, status, logs_path, artifacts_path, started_at, ended_at, commit_sha, branch
- incident
- fields: tenant_id, incident_id, severity, tags, primary_error_type, first_seen, last_seen, frequency, related_stack_trace_ids
Edges and joins
- stack_trace.frames[].symbol_id -> code_symbol
- code_symbol.last_modified_commit -> commit -> pr
- test_failure.stack_trace_id -> stack_trace
- diff_hunk.symbols_touched -> code_symbol
- ci_job -> test_failure -> stack_trace -> code_symbol
Represent edges explicitly to enable graph-style expansion. Even if you use a document store, keep a lightweight adjacency collection:
# edge document
from_id: 'stack_trace:abc123'
to_id: 'code_symbol:repoA/src/foo.py:Foo.bar'
relation: 'mentions'
weight: 0.9
from_id: 'code_symbol:...'
to_id: 'pr:42'
relation: 'touched_by'
weight: 0.8
Normalization tips
- Map frame file paths to repo canonical paths; resolve symlinks and monorepo workspaces
- Deduplicate identical stack traces by error signature + top N frames; track frequency counter
- Merge PR review comments into the diff_hunk context; often the rationale for a change is in comments
- Extract configuration references (flags, env keys) with simple regex into a normalized table; they are frequently the cause of flaky tests and env-specific errors
Ingestion pipelines
You want deterministic, resumable ETL with clear SLAs. Dagster or Airflow works well; for simpler orgs, a handful of idempotent workers is enough.
Connectors
- Git hosting: GitHub/GitLab APIs for PRs, commits, review comments, and diff patches
- CI: GitHub Actions, GitLab CI, Jenkins, CircleCI; collect logs, artifacts, and JUnit XML
- Error tracking: Sentry-like webhook or your own error aggregator. Even plain stderr logs from Kubernetes can work if you parse them
Example: minimal GitHub Actions step exporting JUnit XML and logs to object storage and posting metadata to your RAG service:
yamlname: test on: [push, pull_request] jobs: unit: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.11' - run: pip install -r requirements.txt - run: pytest -q --maxfail=1 --disable-warnings --junitxml=report.xml - name: upload artifacts uses: actions/upload-artifact@v4 with: name: junit path: report.xml - name: notify rag run: | curl -sSf -X POST ${{ secrets.RAG_URL }}/ingest/ci \ -H 'Authorization: Bearer ${{ secrets.RAG_TOKEN }}' \ -F repo_id=${{ github.repository }} \ -F commit_sha=${{ github.sha }} \ -F branch=${{ github.ref_name }} \ -F ci_job_id=${{ github.run_id }} \ -F workflow_name='unit' \ -F junit_xml=@report.xml \ -F logs=@$GITHUB_STEP_SUMMARY
Parsing stack traces
For each language, implement a small parser producing canonical frames. Keep it simple; you can expand later.
- Python: parse lines matching
File path, line N, in function
and the following code context - Java: parse
at package.Class.method(File.java:line)
- Node: parse
at function (file:line:col)
and support sourcemaps if present - Go: parse function lines and next source lines
Attach language and in_repo flags for filtering. For each frame, attempt to resolve to a code_symbol by scanning your symbol index (built from ctags, tree-sitter, or LSP servers).
Example Python-ish sketch:
pythonimport re PY_FRAME = re.compile(r'File (.+), line (\d+), in (.+)') def parse_python_trace(trace_text): frames = [] for line in trace_text.splitlines(): m = PY_FRAME.search(line) if not m: continue path, line_no, func = m.group(1), int(m.group(2)), m.group(3) frames.append({ 'file_path': path, 'line': line_no, 'function': func, 'language': 'python', }) return frames
Parsing test failures
Most frameworks export JUnit XML. Extract suite, classname, test name, failure type, and message.
pythonfrom xml.etree import ElementTree as ET def read_junit(path): doc = ET.parse(path) failures = [] for tc in doc.iterfind('.//testcase'): failure = tc.find('failure') or tc.find('error') if failure is None: continue failures.append({ 'test_suite': tc.get('classname'), 'test_name': tc.get('name'), 'failure_type': failure.get('type') or 'Failure', 'failure_message': (failure.text or '').strip()[:4000], }) return failures
Git diffs and PR history
For each PR, persist:
- Hunk-level patches
- Touched symbols (map by line range overlap)
- A short summary of each hunk, generated offline with a small local model
- Review threads, keyed by file and range
- Temporal metadata: created_at, merged_at, deployment windows if available
Chunk diffs at the hunk level; this is the natural unit to retrieve later.
Symbol index
Produce code_symbol docs by scanning all repositories.
- Use tree-sitter or ctags to enumerate functions, methods, classes, and their line ranges
- Store signature, language, and an excerpt (e.g., first 30 lines)
- Maintain a per-repo, per-branch index, and optionally per-commit snapshots for hot branches
Indexing: hybrid and structure-aware
Search for debugging is not purely semantic. The top-1 signal is often the exact error token or identifier match. The second is temporal proximity. Semantic similarity helps connect noisy failure messages to relevant PR commentary or config toggles. Use a hybrid approach:
- Lexical index (BM25 or equivalent): for error tokens, identifiers, file paths
- Vector index: embeddings per doc chunk, with model specialized or at least competent on code and diffs
- Metadata filters: repo, branch, language, visibility
- Multi-vector strategy: create multiple embeddings per doc: raw text, code-only, and summary. Use multi-vector retrieval if your vector DB supports it
- Graph edges: for post-retrieval expansion (e.g., hunk -> corresponding code_symbol -> PR)
Embeddings
Good defaults as of 2024:
- Code-aware embeddings for code and diffs: options include Jina Code v2, Voyage code, or e5-base for mixed text/code
- General text embeddings for error messages and PR comments: text-embedding-3-large or comparable private model
- Keep dimensionality ~768–1024; HNSW index with M ~ 16–32, efConstruction ~ 128–256
Store per-chunk vectors with fields:
- doc_id, chunk_id
- type: stack_trace, test_failure, diff_hunk, code_symbol, pr_comment
- text, code, summary vectors
- metadata: repo_id, branch, commit_sha, file_path, symbol_id, timestamp, severity
Lexical index
- Tokenize identifiers by camelCase and snake_case splitting
- Preserve file paths and error codes as exact tokens
- Maintain per-repo shards to keep posting lists tight
Temporal weighting
Boost items that occurred around the failure window (e.g., merged within last 30 days). Temporal recency frequently correlates with causality.
Retrieval: turning a failure into a context plan
Retrieval starts with a query builder that takes a single failure and produces multiple sub-queries with weights. For example, given a test failure with a stack trace:
- Query A: error_type + normalized error_message tokens (lexical boost)
- Query B: top stack frame symbol_id and file_path (lexical + vector)
- Query C: recent PR hunks touching the file_path or symbol_id (temporal boost)
- Query D: test_name and suite for known flakiness or previous fixes
Pseudocode outline:
pythondef build_queries(failure, repo_id, branch): tokens = important_tokens(failure['failure_message']) frames = failure['frames'][:3] q = [] # A: error tokens q.append({ 'chan': 'lexical', 'query': ' '.join(tokens), 'filters': {'repo_id': repo_id, 'branch': branch}, 'weight': 1.0, }) # B: top frame symbol or file for fr in frames: q.append({ 'chan': 'hybrid', 'query': f"{fr.get('function') or ''} {fr['file_path']}", 'filters': {'repo_id': repo_id, 'branch': branch}, 'weight': 1.2, }) # C: recent PRs touching these paths for fr in frames: q.append({ 'chan': 'temporal', 'query': fr['file_path'], 'filters': {'repo_id': repo_id, 'branch': branch, 'type': 'diff_hunk', 'merged_after_days': 45}, 'weight': 1.3, }) # D: test name q.append({ 'chan': 'lexical', 'query': f"{failure['test_suite']} {failure['test_name']}", 'filters': {'repo_id': repo_id}, 'weight': 0.7, }) return q
Execute each sub-query, take top-k (e.g., k=25 per channel), then dedupe and rerank globally with a cross-encoder tuned for code and diffs. A good, simple reranking input pairs the failure synopsis with each candidate chunk:
- Left: normalized failure summary including error_type, top 2 frames, test_suite/test_name
- Right: candidate text (diff hunk summary + patch, code excerpt, or PR comment)
Keep final context small but rich: 10–20 chunks is plenty when each chunk is specific (hunks, symbol excerpts, review comments). Add a few graph expansions: for each selected diff_hunk, pull the code_symbol and one parent hunk for surrounding context.
Mandatory filters and negative constraints
Always filter by repo_id and branch unless you intentionally search across repos. Avoid cross-tenant leakage. Add negative constraints:
- Exclude generated files and vendored dependencies unless the stack trace points there
- Exclude secrets or files tagged sensitive
Aggregated context pack
Construct a structured context object for the generator, not just a blob of text:
pythoncontext = { 'failure': { 'error_type': 'AssertionError', 'message': 'expected 200 got 500', 'top_frames': [ {'file': 'src/api/order.py', 'line': 128, 'func': 'create_order'}, {'file': 'src/db/tx.py', 'line': 42, 'func': 'commit'}, ], 'test': {'suite': 'tests.api', 'name': 'test_create_order_happy_path'} }, 'candidates': { 'diff_hunks': [...], 'symbols': [...], 'pr_comments': [...], }, 'repo': {'id': 'shop/api', 'branch': 'main', 'commit_sha': 'abc123'}, }
The model can be steered with tool calls more reliably when fed structured context.
Generation: from context to a patch that actually lands
Generation is where many systems fail by over-editing or hallucinating. Keep it disciplined.
Output contract
Ask the model for a minimal unified diff patch plus a short rationale. Use a strict schema, or function-calling if available.
- Files changed must exist and be within retrieved files unless explicitly allowed
- Max changed lines per file (e.g., 30)
- No new dependencies unless the context includes a relevant PR that added them
Prompt skeleton (abridged):
System: You are a senior engineer tasked with proposing a minimal patch to fix the described failure. Only edit files in the allowed set. The patch must compile and satisfy static checks.
User:
- Failure summary: ...
- Top frames: ...
- Test: ...
- Allowed files: src/api/order.py, src/db/tx.py
- Retrieved context (diff hunks, symbols, PR notes):
1) Diff hunk summary: ...
2) Code excerpt: ...
Return:
- Rationale (3 bullet points)
- Patch (unified diff)
- Targeted tests to run (list)
Constrained decoding
If you can, constrain output to a patch grammar to avoid leakage or chatty outputs. At minimum, enforce a post-processor that:
- Validates unified diff format
- Ensures only allowed files were modified
- Checks line ranges exist and apply cleanly
Sandbox execution and test plan
Before surfacing any patch to humans, run it in an ephemeral, isolated workspace:
- Clone repo at commit_sha
- Apply patch with three-way merge if necessary
- Run a minimal test plan: failing test + its direct dependents + smoke tests
- Run static checks: linters, type checkers
A small orchestrator sketch:
pythonimport subprocess, tempfile, os, json def apply_and_test(repo_url, commit_sha, patch_text, tests): work = tempfile.mkdtemp() subprocess.check_call(['git', 'clone', '--filter=blob:none', repo_url, work]) subprocess.check_call(['git', 'checkout', commit_sha], cwd=work) patch_path = os.path.join(work, 'fix.patch') with open(patch_path, 'w') as f: f.write(patch_text) # apply patch subprocess.check_call(['git', 'apply', '--index', patch_path], cwd=work) # install and run targeted tests r = subprocess.call(['pytest', '-q'] + tests, cwd=work) return r == 0
Tool use: repo-aware actions
Expose a minimal toolset to the model or the orchestrator:
- read_file(path, start_line, end_line)
- list_callers(symbol_id) or show_refs(path, symbol)
- run_tests(test_names)
- format_code(file)
Whether the model or the orchestrator calls tools is an architectural choice. A pragmatic approach: do retrieval and tool execution outside the model and keep the model focused on code synthesis and small planning steps.
Guardrails: keep changes safe and reviews happy
Guardrails turn a clever demo into a dependable assistant.
Policy constraints
- Only modify files surfaced by retrieval or within N lines of a retrieved symbol
- Max total lines changed per patch (e.g., 80)
- No changes to public API signatures unless test clearly indicates backward-incompatible bug and there is prior art in context
- Require successful local tests and lints
Static analysis and type checks
Gate the patch through:
- Language-specific linters (flake8, eslint, golangci-lint)
- Type checkers (mypy, tsc)
- Security scanners for obvious anti-patterns (e.g., disable TLS verification)
Diff risk scoring
Score a patch before running tests. High-risk diffs trigger safe-mode (analysis-only comment or small refactor suggestion).
Signals for risk:
- Touches many files or widely used symbols (from call graph)
- Changes in core packages (e.g., auth, billing)
- Low retrieval confidence (reranker scores) or no matching prior PR context
- Adds or removes conditionals around critical paths
PII and secret hygiene
- Redact secrets before indexing logs
- Strip request bodies and cookies unless explicitly allowed
- Apply tenant isolation: ACL checks on every retrieval and before attaching context to prompts
Evals: prove it works and keeps working
RAG systems drift. Evaluations must be continuous and actionable.
Offline evals
Build a corpus that mimics your org’s failures:
- Real failures: sample CI jobs and incidents with known human fixes
- Synthetic faults: mutation testing (e.g., mutmut for Python, PIT for Java) to create controlled, fixable bugs
For each case, store:
- Inputs: stack trace, failing test XML, repo state
- Gold contexts: hunks, symbols, or PRs that humans consulted (derived from blame and PR references)
- Gold fix: final patch and test outcomes
Metrics:
- Retrieval: recall@k, nDCG@k against gold contexts; time-to-first-relevant
- Generation: patch success rate (applies cleanly and fixes failure), test pass rate, minimality (LOC changed), revert rate (proxy through manual review feedback if available)
- Safety: static check pass rate; false edit rate (patch produced when only analysis was allowed)
Ablations to run:
- With vs without PR comment contexts
- With vs without temporal boost
- Hybrid vs vector-only
- Reranker families: general text vs code-aware
Online evals
- Shadow mode: generate patches but never post; compare against human fixes
- Canary deployments on low-risk repos or branches
- A/B during work hours only; respect developer load
Track:
- Mean time to mitigation on flaky vs deterministic failures
- Developer acceptance rate (patch merged or used as starting point)
- Regression incidents linked to AI-suggested patches
Latency and cost: budgets that fit CI
You need results under a few minutes to be useful during CI or within an IDE session. A concrete budget:
- Ingestion and parsing: amortized, but within 30–60 s of job completion
- Retrieval per query: 50–150 ms for lexical + vector; 150–300 ms with reranker on top 100 candidates
- Generation: 1–8 s depending on model and patch size
- Sandbox: targeted tests under 60–120 s; keep it small by selecting only failing tests and closest dependencies
Cost levers:
- Cache embeddings for recurring error patterns and top frames
- Use a small local reranker for candidate narrowing; run a larger reranker only if scores are borderline
- Prefer local or VPC-hosted models for generation where compliance demands it; use distillation to smaller code models for patch formats
Tooling stack: boring, reliable choices
- Vector DB: Qdrant, Weaviate, Milvus, or pgvector with HNSW. Pick one with hybrid search support and per-collection filters
- Orchestration: Dagster or Airflow for ETL; Celery or a lightweight queue for on-demand patch runs
- LLM serving: vLLM or TGI for on-prem; or use a vendor with VPC peering
- Reranking: a cross-encoder that understands code and diffs; Cohere rerank or a fine-tuned MiniLM/CodeT5+ variant
- Parsing and symbols: tree-sitter for robust, multi-language ASTs
- Telemetry: OpenTelemetry traces for end-to-end requests; Prometheus for RAG-specific counters (retrieval hits, reranker distributions, patch outcome labels)
Worked example: a failure to a minimal patch
Scenario: a Python service fails a test.
- Failure: tests.api::test_create_order_happy_path fails with AssertionError: expected 200 got 500
- Stack trace: top frame in src/api/order.py: create_order at line 128; next frame src/db/tx.py: commit line 42
Retrieval plan:
- Lexical search for error tokens: AssertionError, expected 200 got 500, create_order
- Hybrid search for file path src/api/order.py and symbol create_order
- Temporal search for PRs touching src/api/order.py in the last 45 days
Results:
- diff_hunk A: PR 842 changed error handling in create_order; added a try/except that returns 500 on any exception
- pr_comment: reviewer noted a missing validation before DB commit
- code_symbol: create_order signature shows it returns a tuple (payload, status)
Context summary to the model suggests that we should catch only a specific exception or move validation before the commit.
Proposed patch (sketch):
--- a/src/api/order.py
+++ b/src/api/order.py
@@ def create_order(req):
- try:
- order = validate_and_build(req)
- db.save(order)
- tx.commit()
- return {'id': order.id}, 200
- except Exception as e:
- logger.exception('create_order failed: %s', e)
- return {'error': 'internal'}, 500
+ order = validate_and_build(req)
+ try:
+ db.save(order)
+ tx.commit()
+ except DbConflictError as e:
+ logger.info('conflict: %s', e)
+ return {'error': 'conflict'}, 409
+ return {'id': order.id}, 200
Sandbox run:
- Apply patch
- Run tests.api::test_create_order_happy_path and tests.api::test_create_order_conflict
- Lints and type checks pass
Guardrail checks:
- Lines changed under limit
- Allowed files only
- Diff risk low (single endpoint, two symbols)
The assistant posts the patch to a draft PR with rationale: restoring 200 path for happy case, narrowing exception scope to DbConflictError, and moving validation out of the blanket try/except.
Privacy and security: private by construction
- Tenant isolation: every document is tagged with tenant_id and repo_id; all queries must include tenant_id filters
- Encryption: at rest for object storage and vector DB; in transit with mTLS
- Egress control: no external calls from generation path without explicit allowlists; build-time checks in CI
- Audit: log every retrieval and generation event with hashed context IDs, not raw content
Failure modes and mitigations
- Wrong branch context: always include branch in filters; if missing symbol on current branch, fallback to commit_sha snapshot
- Stale index: incrementally update on every merge; schedule nightly re-index for symbols
- Flaky tests: detect historical flakiness and switch to analysis-only mode with a proposed flake quarantine plan
- Over-broad patches: cap line count, penalize large diffs in scoring, require stronger retrieval evidence when expanding beyond top-1 frame
- Non-reproducible errors: run in container images matching CI; if still non-reproducible, switch to analysis-only report with environment diff suggestions
Minimal service skeleton
Expose a simple endpoint for CI or incident responders to request a fix proposal.
pythonfrom fastapi import FastAPI, Body app = FastAPI() @app.post('/propose-fix') async def propose_fix(payload: dict = Body(...)): # 1) Build failure object failure = payload['failure'] repo = payload['repo'] # 2) Retrieve context queries = build_queries(failure, repo['id'], repo['branch']) candidates = retrieve_and_rerank(queries) context = assemble_context(failure, candidates, repo) # 3) Ask model for patch under constraints patch = generate_patch(context) if not patch: return {'mode': 'analysis_only', 'reason': 'no_safe_patch'} # 4) Sandbox ok = apply_and_test(repo['url'], repo['commit_sha'], patch['diff'], patch['tests']) if not ok: return {'mode': 'analysis_only', 'reason': 'tests_failed', 'patch': patch} # 5) Return patch and rationale return {'mode': 'patch', 'patch': patch}
Configuration example for connectors in YAML:
yamlrepos: - id: shop/api url: git@github.com:org/shop-api.git language: python default_branch: main connectors: ci: provider: github_actions bucket: s3://org-ci-artifacts code_host: provider: github app_installation_id: 123456 retrieval: vector_db: qdrant embedding_models: code: jina-code-v2 text: text-embedding-3-large reranker: cohere-rerank-3 k: 25 final_k: 15 temporal_days: 45 policy: max_lines_changed: 80 allowed_file_patterns: - '^src/' - '^tests/'
Roadmap: compounding gains
- Better localization: train a small classifier to predict the most probable fix file from failure features
- CRAG (compressive RAG): pre-summarize long PR threads into factual kernels to shrink context while preserving rationale
- Delta indexing: update only changed symbols and hunks per merge to keep ingestion cheap
- Self-play: generate synthetic bugs by mutating recent diffs, then let the system fix them; add successful cases to training/eval sets
- IDE integration: let developers trigger propose-fix locally with cached retrieval contexts; upload sanitized traces when allowed
Final take
Most debugging is a retrieval problem disguised as code generation. Once you feed the model the right hunks, symbols, and review notes, the patch is usually surgical. The craft is in building a private, relation-aware retrieval stack; constraining generation to minimal patches; and wrapping it with tests and policy gates that mirror how your team already works.
Start small: index your last 90 days of PRs and CI failures, wire up a strict patch contract, and run in shadow mode. Measure retrieval recall@k and patch success on a fixed set of incidents. Iterate on chunking and reranking before swapping models. The fastest path to reliable, org-aware fixes is a boring, well-instrumented RAG pipeline that treats logs and diffs as first-class citizens.