# Build a living architecture doc and code glossary for Laravel + React

> Intro — keep your codebase understandable without hiring a docs teamSolo founders and small teams building Laravel + React SaaS need a lightweight, always-updat...

- Source: https://vibe-coding-saas.nicheflash.com/blogs/living-architecture-doc-code-glossary-laravel-react
- Publisher: Vibe Coding SaaS
- Published: 2026-05-10
- Updated: 2026-08-02

## Intro — keep your codebase understandable without hiring a docs team

Solo founders and small teams building Laravel + React SaaS need a lightweight, always-updated architecture doc and code glossary so onboarding, debugging, and feature scoping stay fast. This post gives a pragmatic, AI-assisted workflow you can ship in a week: repository-level summaries, per-component glossaries, and ADR stubs, with clear trade-offs and actionable prompts.

### Why a living architecture doc matters (fast ROI)

A living architecture doc reduces context-switch time, lowers onboarding time, and captures why decisions were made — not just what. Using copilots or repo-chat to summarize modules is a proven starting point for documenting legacy or unfamiliar codebases; try a quick file-level summarization flow and commit the result for review to make value visible fast [1].

### Core approach: hierarchical summarization + RAG grounding

At repo scale use a hierarchical summarization pipeline: file-level summaries → directory/module syntheses → component and system-level overviews. This pattern lets you use smaller/local LLMs effectively and prevents sending your entire codebase to a single remote model, which helps control cost and leakage risk [2]. Pair summaries with a vector index for retrieval-augmented generation (RAG) and namespace metadata so queries can be scoped to services, environments, or teams.

### Practical workflow (what to build)

1. **Index & chunk** — crawl README, controllers, routes, key React components, migrations, and tests. Save chunks with metadata: path, component, language, sensitivity.
2. **Embed & store** — insert embeddings + metadata into a vector DB and use namespaces to separate environments or feature branches.
3. **Summarize hierarchically** — generate file summaries, synthesize per-folder docs, then make a component-level doc and a 1-line ADR stub explaining why it exists.
4. **Publish under docs/** — write outputs to docs/ with dated filenames and YAML front matter (author, generator, date) so all changes are reviewable and reversible.
5. **Enforce access & hygiene** — apply retrieval-time authorization and a clear AGENTS.md / HOW_TO_DOC.md so agents write only where allowed [3][4][5].

### One-day checklist (quick win)

1. Run Copilot or repo-chat on a small module and ask: "Summarize this module in 3 bullets." Commit the result to *docs/module-summary.md* for review; this creates immediate value and a human-checkpoint [1].
2. Add *docs/HOW_TO_DOC.md* with rules for agent-created files (naming, location, front matter) and a pre-commit hook to block docs at repo root [5].
3. Spin up a free-tier or local vector index (e.g., Qdrant) and insert 100–1,000 chunks to practice retrieval and filters.

### One-week plan (ship a living doc)

1. Day 1–2: Crawl repo, chunk files, generate file-level summaries using concise prompts (examples below).
2. Day 3: Synthesize component-level docs and ADR stubs; commit to *docs/components/* with YAML front matter.
3. Day 4: Wire a simple repo-chat endpoint that queries your vector index and returns grounded answers referencing docs + code.
4. Day 5–7: Add access-control filters and a PR checklist requiring human review of AI-generated content; iterate on prompts and chunk sizes.

### Suggested prompts and patterns

Keep prompts short and structured so outputs are predictable and reviewable. Examples you can copy:

- **File summary (20–40 words):** "Summarize the purpose of this file in 2 sentences; list exported functions and side effects; output in YAML with keys: summary, exports, side_effects."
- **Component synthesis:** "Combine these 6 file summaries into a 3-paragraph component doc: purpose, key flows, testing notes. Add a 1-line ADR stub that lists alternatives considered."
- **Glossary entry:** "Create a glossary entry for domain term 'invoiceRun' with: definition, canonical location (file path), related tests, and examples of usage."

### Security and trade-offs: local vs cloud

Local or smaller LLMs reduce data leakage risk and running cost but can miss subtle semantics; cloud models provide higher-quality synthesis but increase exposure and token costs. For production RAG systems implement authorization at retrieval time (pre-filter or post-filter) and treat the vector index as a sensitive boundary — use metadata filters, frequent resyncs, or ReBAC-style controls to prevent overexposure [2][3][4].

### Agent hygiene and repo conventions

Stop agents from littering the repo by enforcing simple conventions: require outputs under *docs/*, use YYYY-MM-DD prefixes, and require YAML front matter with author and agent name. Publish a single AGENTS.md that every agent must read before writing and add automated checks to block nonconforming files [5].

### Real-world example

Kapwing used company-wide coding agents to let non-engineers ship low-complexity changes while keeping engineers responsible for reviews; they combined small scoped tasks, automation (agent → PR → dev deploy), and training so agents scaled work without creating chaos — a pattern you can mirror for doc generation with human review gates [6].

### Conclusion — keep docs inside your workflow

Start small: index one component, auto-generate file summaries with a constrained prompt, synthesize a component doc and ADR stub, and commit under *docs/*. Enforce simple agent rules and retrieval-time authorization so your living architecture doc becomes a durable asset that preserves velocity, lowers onboarding time, and keeps your Laravel + React codebase understandable as it grows [1][2][3][5].

## References

1. [[1]](https://github.blog/ai-and-ml/github-copilot/documenting-and-explaining-legacy-code-with-github-copilot-tips-and-examples/)
2. [[2]](https://www.aimodels.fyi/papers/arxiv/hierarchical-repository-level-code-summarization-business-applications)
3. [[3]](https://www.pinecone.io/learn/rag-access-control/)
4. [[4]](https://aws.amazon.com/blogs/security/authorizing-access-to-data-with-rag-implementations/)
5. [[5]](https://solmaz.io/2025/12/22/how-to-stop-ai-agents-from-littering-your-codebase-with-markdown-files/)
6. [[6]](https://www.kapwing.com/blog/how-we-achieved-100-adoption-of-ai-coding-agents/)