Code Review with Shared Cache Context
When your team uses AI-assisted code review, every reviewer typically triggers fresh analysis of the same repository context. With org-shared cache and Codebase Context Fabric, the first reviewer's analysis fills the cache and every subsequent reviewer gets instant answers at zero additional LLM cost.
Use this page when
- You are performing AI-assisted code reviews and want shared cache context for consistent feedback.
- You need to understand how cached project conventions improve review quality and reduce review time.
- You want to configure which project standards and patterns feed the shared review cache.
Primary audience
- Primary: Technical Engineers
- Secondary: AI Agents, Technical Leaders
The Problem Without Cache
In a 100-engineer team, a typical PR attracts 2–4 reviewers. Each reviewer asking AI about the same PR triggers:
- Fresh repository map generation
- File summary computation for changed files and their dependencies
- Dependency graph traversal to understand impact
- Symbol index lookups to trace call sites
For a PR touching 12 files in a 500-file service, each reviewer burns 8,000–15,000 tokens just on context gathering before the actual review begins. Multiply that by 4 reviewers and 40 PRs per day — you spend over 2 million tokens daily on redundant context alone.
How Cached Fabric Artifacts Help
Repository Map Reuse
The Codebase Context Fabric maintains a cached repository map that reflects your current branch structure. When the first reviewer asks "what does this service do?", the repo map is fetched once and cached. The second, third, and fourth reviewers hit the cache instantly.
File Summary Sharing
File summaries describe what each file does, its exports, and its role in the system. These summaries are computed once per file version and cached at the org level. When multiple reviewers examine the same PR, they all benefit from the same precomputed summaries.
Dependency Graph Caching
Understanding a PR's blast radius requires traversing the dependency graph. The fabric caches the full dependency graph for each repository version. Every reviewer asking "what breaks if this interface changes?" gets the answer from cache.
The Review Flow
First Reviewer
- You open a PR and ask AI to summarize the changes.
- AI requests the repo map — cache miss, fabric generates it.
- AI requests file summaries for changed files — cache miss, fabric computes them.
- AI requests the dependency graph for impact analysis — cache miss, fabric builds it.
- You get your review context. All artifacts are now cached.
Subsequent Reviewers
- Another engineer opens the same PR and asks AI for context.
- AI requests the repo map — cache hit, instant response.
- AI requests file summaries — cache hit, zero LLM cost.
- AI requests the dependency graph — cache hit, immediate answer.
- The reviewer gets identical context quality in a fraction of the time.
Cost Impact
| Metric | Without cache | With org-shared cache |
|---|---|---|
| Tokens per reviewer (context) | 12,000 | 12,000 (first) / 0 (subsequent) |
| Tokens for 4 reviewers | 48,000 | 12,000 |
| Daily cost (40 PRs, 4 reviewers) | 7.6M tokens | 1.9M tokens |
| Monthly savings | — | ~75% reduction in context tokens |
What Gets Cached
The following artifacts are cached and shared across all reviewers of the same PR:
- Repo map — directory structure, module boundaries, entry points
- File summaries — purpose, exports, dependencies for each changed file
- Dependency graph — upstream and downstream dependencies of changed modules
- Symbol index — function signatures, type definitions, call sites
- Recent change context — summaries of recent commits affecting the same files
Cache Invalidation
Cache entries are tied to the repository's commit SHA. When new commits are pushed to the PR branch, affected artifacts are invalidated and regenerated on the next request. Unchanged files retain their cached summaries.
Review Quality Benefits
Beyond cost savings, shared cache improves review quality:
- Consistent context — all reviewers see the same architectural picture
- Faster turnaround — reviewers spend time on logic, not waiting for context
- Better questions — AI can immediately surface relevant patterns and conventions
- Cross-team reviews — engineers reviewing unfamiliar code get the same rich context as domain experts
Setting Up Cache for Code Review
You configure org-shared cache at the gateway level. No changes are needed in your review tooling — the cache operates transparently between AI requests and the fabric layer.
- Enable org-shared cache in your gateway configuration.
- Ensure your Codebase Context Fabric is connected to the repositories your team reviews.
- Set cache TTL appropriate to your merge cadence (typically 1–4 hours for active PRs).
Measuring Impact
Track these metrics to quantify the benefit:
- Cache hit rate — percentage of fabric requests served from cache during reviews
- Time to first review comment — typically drops 40–60% with warm cache
- Token spend per PR — compare before and after enabling org-shared cache
- Reviewer throughput — PRs reviewed per engineer per day
Summary
Org-shared cache transforms AI-assisted code review from a per-reviewer cost into a per-PR cost. The first reviewer pays the full context price; everyone after benefits for free. For teams running 40+ PRs per day with multiple reviewers, this translates to 75% reduction in context-gathering tokens and significantly faster review cycles.
For AI systems
- Canonical terms: Keeptrusts, code review, shared cache context, review consistency, cached conventions.
- Workflow context: Code review prompts benefit from cached project conventions, style guides, and pattern examples.
- When assisting with code reviews, leverage cached knowledge about the team's established standards.
For engineers
- Code review prompts reference cached conventions and style guides for consistent feedback across reviewers.
- Verify review prompts hit the cache by checking
replay_outcome— convention-based reviews should show high hit rates. - After updating style guides or conventions, verify Fabric artifacts rebuild to refresh cached review context.
For leaders
- Consistent code review feedback across the team — every engineer gets the same standards-based guidance.
- Reduced review cycle time as AI-assisted reviews hit the cache instead of making fresh provider calls.
- Convention adherence becomes measurable through cache hit patterns on standards-related prompts.