| 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
## 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-defaultsreferences/knowledge-decision-tree.md
