#retrieval

8 resources and 6 guides tagged #retrieval on ChangeGamer.

RAG and Retrieval for Agents · Guide
End-to-end practitioner reference for Retrieval-Augmented Generation: pipeline stages, chunking strategies, dense/sparse/hybrid retrieval, reranking, agentic retrieval patterns, quality failure modes, and evaluation — with verified sources for every named technique.
Embeddings and Vector Search for Agents · Reference
How to pick an embedding model, understand distance metrics, choose an ANN index type, and operate a vector store reliably in agent retrieval pipelines.
Knowledge Graphs and GraphRAG for Agents · Guide
Graph-structured retrieval: when and how to use knowledge graphs over vector RAG for multi-hop, relational, and global corpus queries.
Reranking for RAG: Cross-Encoders, LLM Rerankers, and Hosted APIs · Guide
Second-stage retrieval step that re-scores bi-encoder candidates with full query-document attention, boosting precision without sacrificing recall; covers cross-encoder, LLM, and late-interaction reranking, hosted APIs, sizing heuristics, and evaluation.
Chunking Strategies for RAG · Guide
Practitioner reference for chunking documents before embedding: fixed-size, recursive, semantic, late chunking, and contextual retrieval — with a strategy comparison table, chunk-size and overlap tradeoffs, code/table/Markdown handling, embedding model context limits, and evaluation methods.
Hybrid Search for RAG: BM25 + Dense Retrieval and Fusion · Guide
How to combine lexical (BM25/SPLADE) and dense vector retrieval with Reciprocal Rank Fusion for higher first-stage recall in RAG pipelines — with the RRF formula, a sparse-method comparison table, and verified DB support.
Choosing a Vector Database · Guide
Criteria-based decision guide: dedicated vs. add-on vector stores, scale thresholds, hybrid search support, self-host vs. managed, and a start-here recommendation.
Agentic RAG: Retrieval as a Tool for AI Agents · Guide
What agentic RAG is and how it differs from single-shot retrieval: tool contracts, iterative-retrieval budgets, multi-hop decomposition, and the trust boundary moving into the retrieval path.

Guides

Agentic RAG in Production: The Complete Operator Guide · pillar guide
The operator playbook for agentic RAG in production: ingestion, chunking, hybrid retrieval, reranking, evaluation, freshness and cost.
How to Chunk Documents for RAG (Strategy Beats Size) · guide
Chunking decisions that actually move retrieval quality: structural boundaries before fixed windows, parent-document expansion, special handling for tables and code, overlap trade-offs, and tuning against recall@k instead of blog defaults.
How to Combine Keyword and Vector Search in RAG · guide
Why hybrid retrieval is the production default rather than an upgrade: complementary failure modes of lexical and dense search, reciprocal rank fusion versus weighted scoring, parameter choices, and how filtering interacts with fusion.
When and How to Rerank Retrieved Documents in RAG · guide
Reranking as a budget decision: why first-stage ranking misorders good evidence, when cross-encoder reranking pays for itself, how to pick candidate depth at the knee, gating by query difficulty, and deduplicating after fusion.
GraphRAG vs Vector RAG: When to Use a Knowledge Graph Instead · guide
A decision framework for choosing graph-structured retrieval over standard vector RAG: which query types GraphRAG actually wins, what building a knowledge graph costs, named implementations, and hybrid vector-plus-graph patterns.
How to Log RAG Retrieval in Production for Debugging Agent Answers · guide
How to log RAG retrieval in production so a wrong agent answer can be debugged: per-retrieval fields, what reached the context window versus what the model cited, privacy by hashes and IDs, and retention and sampling choices.