gbrain/test/e2e/fixtures/concepts/retrieval-augmented-generation.md

type, title, aliases, tags

type	title	aliases	tags
concept	Retrieval-Augmented Generation	RAG 検索拡張生成	ai search architecture

Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is a technique that enhances large language model responses by retrieving relevant documents from a knowledge store and including them as context in the prompt. Also known in Japanese as 検索拡張生成 (RAG).

How It Works

1. Query embedding — The user's query is converted into a vector embedding using a model like OpenAI's text-embedding-3-large.
2. Retrieval — The query vector is compared against stored document vectors using similarity search (typically cosine similarity). The top-k most similar documents are retrieved.
3. Context stuffing — Retrieved documents are inserted into the LLM prompt as context, giving the model access to specific, relevant knowledge.
4. Generation — The LLM generates a response grounded in the retrieved context rather than relying solely on its training data.

Advantages

• Grounds LLM responses in specific, up-to-date knowledge
• Reduces hallucination by providing factual context
• Allows knowledge to be updated without retraining the model
• Scales to large knowledge bases with efficient vector indexing

Limitations

• Quality depends heavily on retrieval accuracy — if the wrong documents are retrieved, the answer will be wrong or incomplete
• Pure vector search can miss exact keyword matches (the "vocabulary mismatch" problem)
• Chunk boundaries can split important context across fragments
• No synthesis: retrieved chunks are raw fragments, not curated knowledge

GBrain's Approach

GBrain uses RAG as its core query mechanism but addresses several standard RAG limitations through deliberate design choices:

• Compiled truth pages mean retrieved content is pre-synthesized knowledge rather than raw note fragments. This is the key differentiator from standard RAG systems.
• Hybrid search combines vector similarity with keyword full-text search using Reciprocal Rank Fusion (RRF), addressing the vocabulary mismatch problem.
• Multi-query expansion generates multiple search queries from a single user question to improve recall.
• Deduplication ensures the same content is not retrieved multiple times when it matches across different query expansions.

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Timeline

2025-02-15 — RAG Research

Evaluated standard RAG patterns for GBrain. Identified the core tension: RAG works best when retrieved documents are high quality and self-contained, but most note-taking systems produce fragmented, partially-overlapping content. This led to the compiled truth pattern as a write-time optimization for read-time retrieval quality.

2025-03-28 — Hybrid Search Decision

During weekly sync, decided to implement hybrid search (vector + keyword with RRF) for GBrain v0.3. Pure vector search was missing exact keyword matches, and pure keyword search was missing semantic near-matches. Hybrid search with Reciprocal Rank Fusion gives us the best of both approaches.