LLM Wiki
An LLM reads the corpus and discovers its topics, writing a synthetic wiki article per topic. Queries match against article embeddings by cosine similarity, and answers are synthesized from whole articles rather than fragments — knowledge that compounds as documents are added.
Stage-by-Stage Data Flow Explorer
Select a phase from the controller below, then click individual step nodes to view their technical role, inputs, outputs, and mockup diagnostics data stream.
Ingestion
The LLM clusters the corpus into topics and authors an article for each one.
> INGEST_STREAM: "financial_report_2026.pdf" (Size: 2.4 MB)
> DECODING_META: { mime: "application/pdf", pages: 12 }
> READOUT: "Ragiment Corp Annual Report 2026. EBITDA grew 18% to $4.6M. Product lines expanded by..."Best suited for
Static, knowledge-dense corpora queried many times — build once, query forever.
Complexity
A costly LLM build step — topic discovery plus an article synthesized per topic — layered on top of a standard vector pipeline.
Relevance today
A newer, opinionated pattern (inspired by Karpathy's “LLM wiki” idea) — niche today, but compelling for static, knowledge-dense corpora.
Where it's used
Onboarding & training
Synthesized topic articles give newcomers coherent, readable knowledge.
Documentation portals
Serve whole-topic pages instead of scattered fragments.
Long-lived research corpora
Knowledge compounds and improves as more documents are added.
Why it matters
- Answers come from coherent, whole-topic articles rather than disjointed chunks.
- High query-time quality because the heavy synthesis happens once at build time.
- Knowledge compounds — adding documents enriches existing articles.
Trade-offs & considerations
- Expensive one-time build: the LLM synthesizes an article for every discovered topic.
- Best for static corpora; frequent updates trigger costly re-synthesis.
- Answer quality depends on how well topic discovery clusters the corpus.
Alternatives to consider
When LLM Wiki isn't the right fit, reach for one of these instead.