#rag

12 resources and 1 guide tagged #rag on ChangeGamer.

Agent Memory and Context Management · Guide
Architecture reference for agent memory: types (working, long-term, episodic, semantic, procedural), context-management techniques (summarization, RAG, sliding windows, prompt caching), storage substrates, and memory frameworks — with security notes and cross-links to related guides.
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.
Web Data and Scraping for Agents · Reference
Tool landscape for agent web-data pipelines: reader/URL-to-Markdown APIs, crawl/scrape services, and search APIs — with MCP exposure, OSS/SaaS classification, and practical guidance.
Document Extraction and Parsing for Agents · Reference
Practitioner reference for the document-ingestion pipeline agents use: parse/OCR, layout/structure extraction, schema-constrained field extraction — with a verified tooling landscape (OSS and cloud).
Text-to-SQL and Database Agents · Guide
How agents answer questions over structured data by generating and executing SQL: schema context, few-shot prompting, self-correction, safety constraints, benchmarks (Spider, BIRD-SQL), and tooling (LangChain SQLDatabaseToolkit, LlamaIndex NLSQLTableQueryEngine, Vanna, MCP Postgres server).
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.
Fine-Tuning vs RAG vs Prompting · Guide
Decision guide for agent builders: when to use prompting, RAG, or fine-tuning — and how they combine. Covers SFT, LoRA/QLoRA, DPO, distillation, and a symptom-to-fix table.
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.

Guides

How AI Search Engines Choose Sources (And What You Can Actually Influence) · guide
What is known, what is claimed and what is speculation about how ChatGPT, Perplexity and AI Overviews pick the pages they cite — and the short list of things a site owner can actually control.