Standard RAG
Documents are chunked, embedded, and indexed twice — once as dense vectors, once as a BM25 keyword index. At query time both retrievers run in parallel, their results are merged, reranked by a cross-encoder, and the best chunks are handed to the LLM as grounded context.
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
Load documents, split them into overlapping chunks, embed each chunk, and store the vectors.
> 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
The production default — general question-answering over a medium-to-large document corpus.
Complexity
A handful of well-understood stages — chunk, embed, retrieve, rerank, generate. The easiest pipeline to operate, debug, and reason about.
Relevance today
The most widely deployed RAG architecture and the right default for almost every project — the baseline everything else is measured against.
Where it's used
Documentation & support Q&A
Answer questions over product docs, manuals, and help centres with grounded citations.
Internal knowledge bases
Search Confluence, Notion, or SharePoint and return sourced answers instead of links.
Customer-facing chatbots
Ground every response in a known corpus, keeping the model from inventing facts.
Why it matters
- Hybrid dense + BM25 retrieval catches both semantic matches and exact keywords, codes, and names.
- Cross-encoder reranking lifts precision before the LLM sees context — fewer hallucinations.
- Provider- and database-agnostic; scales from a laptop to millions of vectors.
Trade-offs & considerations
- Struggles with multi-hop questions that connect facts across documents — reach for GraphRAG.
- Requires a vector database to run, unlike the zero-infra PageIndex pipeline.
- Chunking strategy materially affects quality — tune chunk size and overlap for your documents.
Alternatives to consider
When Standard RAG isn't the right fit, reach for one of these instead.