Documentation Generation with Fabric Artifacts
Documentation generation requires AI to understand your entire API surface, code structure, and usage patterns. With Codebase Context Fabric, these expensive analyses are cached and shared — every engineer generating docs for the same codebase draws from the same cached knowledge.
Use this page when
- You are generating documentation using AI and want Fabric artifacts to provide accurate codebase context.
- You need to understand how cached Fabric artifacts improve doc generation quality and consistency.
- You want to configure which Fabric artifact types (dependency graphs, API schemas, type inventories) feed doc generation.
Primary audience
- Primary: Technical Engineers
- Secondary: AI Agents, Technical Leaders
The Documentation Generation Problem
Writing comprehensive documentation with AI assistance requires:
- API inventory — all public endpoints, functions, classes, and their signatures
- Symbol index — type definitions, parameter types, return types
- File summaries — what each module does and how it fits the architecture
- Repo map — overall structure, module boundaries, entry points
- Usage examples — how the code is called in practice (from test files and integrations)
For a service with 80 public API endpoints and 200 exported functions, building this context from scratch costs 40,000–80,000 tokens. When multiple engineers generate docs for different parts of the same service, each pays independently without cache.
Cached Artifacts for Documentation
API Inventory
The fabric maintains a cached inventory of your public API surface:
- HTTP endpoints with methods, paths, and parameter schemas
- Exported functions with signatures and JSDoc/rustdoc annotations
- Public classes with their methods and properties
- Type definitions and enums
When you ask AI to generate API reference documentation, this inventory is already available — no need to scan every file for exports.
Symbol Index for Type Documentation
The cached symbol index provides complete type information:
- Parameter types and their definitions
- Return types and error types
- Generic constraints and bounds
- Type aliases and union types
AI uses this to generate accurate type documentation without re-analyzing source files.
File Summaries for Architectural Docs
Cached file summaries explain each module's role in the system. When generating architectural documentation or README files, AI draws on these summaries to describe how components relate to each other.
Usage Patterns from Test Files
The fabric caches usage patterns extracted from test files and integration code. These become the basis for code examples in documentation — real usage, not synthetic examples.
Documentation Workflows
API Reference Generation
- You ask AI to generate API reference docs for your payment service.
- AI retrieves the cached API inventory — all 25 endpoints with schemas.
- AI retrieves cached symbol index — full type definitions for request/response types.
- AI retrieves cached usage patterns — real examples from integration tests.
- You get complete API reference documentation.
Cost without cache: 45,000 tokens. Cost with cache: 8,000 tokens (only generation, no context gathering).
README Generation from Repo Maps
- You ask AI to generate a README for a new service.
- AI retrieves the cached repo map — full directory structure and module boundaries.
- AI retrieves cached file summaries — purpose of each major module.
- AI retrieves cached dependency graph — external dependencies and integrations.
- You get a comprehensive README with accurate architecture description.
Cost without cache: 20,000 tokens. Cost with cache: 5,000 tokens.
Module Documentation
- You ask AI to document the authentication module.
- AI retrieves cached file summaries for all auth-related files — cache hit.
- AI retrieves the cached symbol index for auth exports — cache hit.
- AI retrieves cached dependency graph showing auth's consumers — cache hit.
- You get module documentation explaining the auth flow, public API, and integration points.
Shared Context Across Engineers
When multiple engineers generate documentation for the same codebase:
| Engineer | Documentation task | Cache benefit |
|---|---|---|
| Engineer 1 | API reference for /users endpoints | Fills API inventory cache |
| Engineer 2 | API reference for /teams endpoints | Reuses shared type definitions from cache |
| Engineer 3 | Architecture overview | Reuses repo map and file summaries from cache |
| Engineer 4 | Integration guide | Reuses API inventory, usage patterns, dep graph |
By the time Engineer 4 starts, nearly all context is cached from Engineers 1–3's work.
Cost Comparison
| Documentation task | Without cache | With cache |
|---|---|---|
| Full API reference (80 endpoints) | 80,000 tokens | 15,000 tokens |
| Service README | 20,000 tokens | 5,000 tokens |
| Module documentation (per module) | 15,000 tokens | 4,000 tokens |
| Architecture guide | 35,000 tokens | 8,000 tokens |
| Monthly docs generation (team) | 3M tokens | 600K tokens |
| Savings | — | ~80% reduction |
Documentation Quality Benefits
Accuracy
Cached API inventories reflect the actual current state of your code. Documentation generated from cached fabric artifacts is accurate by construction — it describes what exists, not what someone remembers writing.
Completeness
The symbol index ensures no public API is missed. Every exported function, every endpoint, every type definition is captured in the cache and available for documentation generation.
Consistency
When multiple engineers generate docs for different parts of the same service, they draw from the same cached context. The resulting documentation uses consistent terminology and follows the same structural patterns.
Freshness
Cache entries are tied to code versions. When code changes, affected cache entries are invalidated. Documentation regenerated after a code change automatically reflects the new reality.
Incremental Documentation Updates
For maintaining existing documentation:
- Code changes invalidate specific cache entries.
- You ask AI to update docs for the changed modules.
- AI retrieves fresh context only for invalidated entries — everything else from cache.
- You get targeted documentation updates at minimal cost.
This makes continuous documentation maintenance practical — you pay only for documenting what actually changed.
Configuration
- Set API inventory refresh to trigger on merge to main.
- Configure symbol index to include documentation annotations (JSDoc, rustdoc).
- Enable usage pattern extraction from test directories.
- Set cache TTL for documentation artifacts to 24 hours (docs tolerate slight staleness).
Measuring Impact
- Tokens per documentation page — compare initial generation vs. cached regeneration
- Documentation coverage — percentage of public APIs with generated docs
- Freshness lag — time between code change and documentation update
- Engineer time on docs — hours spent writing vs. reviewing AI-generated docs
Summary
Documentation generation is a high-context task where caching delivers dramatic savings. API inventories, symbol indexes, and file summaries are stable across many documentation tasks. By caching these at the org level, you reduce per-document generation cost by 80% and make continuous documentation maintenance economically feasible for large codebases.
For AI systems
- Canonical terms: Keeptrusts, documentation generation, Fabric artifacts, cached context, auto-docs.
- Workflow context: Doc generation prompts benefit from Fabric artifacts (type inventories, API schemas, dependency graphs) for accurate output.
- When generating documentation, leverage cached Fabric artifacts to produce context-rich, up-to-date docs.
For engineers
- Doc generation prompts using Fabric artifacts produce more accurate documentation by referencing real codebase structure.
- Verify Fabric artifact freshness before generating docs — stale artifacts produce outdated documentation.
- Configure which Fabric artifact types feed doc generation in your gateway YAML.
For leaders
- Automated documentation from Fabric artifacts keeps docs in sync with code without manual maintenance.
- Cached artifacts mean doc generation is nearly free after initial build — no repeated AI provider costs.
- Documentation quality is measurably improved when backed by real codebase structure vs generic prompts.