knowledge-architecture
Use when designing knowledge bases, RAG, wiki, knowledge graphs, memory for agents, or "how should we store docs for humans vs agents". Produces a clear consumer/form/retrieval decision. Avoids buzzword soup. Do NOT use for ordinary app CRUD databases.
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- 23 juillet 2026 à 13:47
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SKILL.md
Instructions source · Aperçu en lecture seule- name
- knowledge-architecture
- description
- Use when designing knowledge bases, RAG, wiki, knowledge graphs, memory for agents, or "how should we store docs for humans vs agents". Produces a clear consumer/form/retrieval decision. Avoids buzzword soup. Do NOT use for ordinary app CRUD databases.
# Knowledge Architecture
Separate three decisions people often mash together: **who consumes**, **what form**, **how retrieval works**.
## Step 1 — Who consumes?
| Consumer | Optimize for |
|----------|----------------|
| **Humans** | Readable structure, navigation, editability (Wiki / Markdown tree) |
| **Agents** | Chunkability, metadata, stable IDs, tool-friendly retrieval |
| **Both (default for products)** | Single source of truth humans edit + indexes agents query |
Default bias: **coexistence** — humans write Markdown/Wiki; agents get hybrid retrieval over the same source. Avoid a second shadow knowledge base unless required.
## Step 2 — What form?
| Form | When |
|------|------|
| **Markdown / Wiki** | Most products; docs, runbooks, specs |
| **Structured DB records** | Strong schema entities (tickets, SKUs) |
| **Knowledge graph** | Relationships are first-class (org charts, law cites, multi-hop entity links) |
| **Vector-only corpus** | Large unstructured dump with weak structure needs |
MVP default: **Markdown wiki (or `/docs`)**. Promote to graph when queries are relationship-heavy and hybrid RAG fails.
## Step 3 — Retrieval pattern
| Pattern | When |
|---------|------|
| Full-text only | Small corpus, precise keywords |
| Vector RAG | Semantic questions over unstructured text |
| **Hybrid** (keyword + vector) | Default production RAG |
| **Agentic RAG** | Multi-step: plan → retrieve → critique → retrieve again |
| Graph RAG | Multi-hop entity/relation questions |
MVP default: **hybrid retrieval**. Use agentic RAG when single-shot retrieval quality is measurably insufficient.
## Decision Output
```markdown
## Consumers: human | agent | both
## Source form: markdown-wiki | db | graph | mixed
## Retrieval: fts | vector | hybrid | agentic | graph-rag
## Ingestion path
## Evaluation plan (5 real questions the system must answer)
## Non-goals
```
## Anti-Patterns
- "We need a knowledge graph" with no multi-hop questions
- Separate Notion for humans + random vectors for agents with no sync
- Agentic RAG theater on a 20-page corpus
- Building custom RAG platform instead of using mature components (research-before-build)
## Related
- `research-before-build`, `stack-defaults`
- `references/knowledge-decision-tree.md`
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