#rag

14 resources and 16 guides 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.
Context Window Management: Budgets, Compaction and Caching · Reference
How to work within finite context windows: budget allocation across instructions, evidence and history; truncation versus summarization versus retrieval offload; prefix stability for prompt caching; and attention-quality caveats of very long contexts.
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

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.
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 Build a RAG Ingestion Pipeline That Survives Production · guide
The six properties that separate a production RAG ingestion pipeline from a demo script: tested extraction, idempotent writes, incremental updates, deletion propagation, durable execution, and metadata captured at ingest time.
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 Choose an Embedding Model for RAG (and Version It Like a Schema) · guide
Embedding selection as an operations problem: the criteria that dominate total cost of ownership, the never-mix-spaces invariant, reindex migrations with dual indexes, and where quantization fits once cost shows up.
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.
How to Evaluate a RAG System (Retrieval Metrics, Generation Metrics, CI Gates) · guide
The evaluation harness that keeps RAG changeable: golden-set construction, retrieval metrics separated from generation metrics, LLM-as-judge screening with human acceptance, CI regression gates, and the logged-query flywheel.
How to Keep a RAG Index Fresh (Staleness Bounds, Not Vibes) · guide
Freshness as an engineered property: per-source staleness contracts, document versioning and tombstones, effective-date filtering, deletion propagation with reconciliation backstops, and the sync-lag metrics that predict stale answers before users report them.
Permission-Aware Retrieval: Multi-Tenant RAG Without Leaks · guide
How to enforce access control inside retrieval for multi-tenant RAG: authorization as mandatory pre-filters derived from the authenticated principal, tenant isolation mechanics, permission-negative testing, and the post-filter trap that produces empty answers.
Retrieval as a Tool: Agentic RAG Patterns That Survive Production · guide
When AI agents consume retrieval as a tool rather than a pipeline stage: narrow tool contracts, hard budgets on iterative retrieval, query decomposition, provenance-carrying results, and keeping trust boundaries inside the retrieval path.
How to Cut RAG Cost and Latency Without Cutting Quality · guide
RAG cost and latency as engineered budgets: where the money actually goes, caching layers and their hit-rate economics, routing queries to right-sized models, bounding retrieval fan-out, the hidden lines (reindex migrations, eval compute), and p95 discipline.
Common RAG Failure Modes and How to Fix Them · guide
An operator runbook for the four RAG failure classes with no dedicated deep-dive elsewhere: retrieval miss, context overload, injection via content at ingestion time, and silent quality degradation — symptom, first diagnostic, and fix for each.
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.
When Is RAG the Wrong Answer? A Decision Guide · guide
A worked decision guide for the four real alternatives to retrieval-augmented generation: including knowledge directly, querying structured data with text-to-SQL, fine-tuning for behavior change, and graph-based retrieval for entity relationships.
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.