Advanced RAG

Decision guide for RAG architecture: when to use RAG vs alternatives, what it costs, how the two-pipeline architecture works, how RAG fails in production, and a map of the 22 articles in this topic.

How to choose, tune, and evaluate chunking strategies for RAG. Covers recursive, document-aware, and semantic splitting — plus Contextual Retrieval and Late Chunking, the two post-2024 techniques that address the root cause of most retrieval failures.

The 2026 embedding landscape has 4 commercial providers, a tier of free open-weight models that match commercial quality, and Matryoshka compression that cuts storage 92% with minimal recall loss. This article covers how to choose, evaluate, and migrate embedding models for production RAG.

How to choose a vector database for production RAG in 2026. Six databases compared honestly — quantization changes the cost math, migration is not a one-line change, and most teams will outgrow their first choice at a predictable threshold.

Hybrid search (BM25 + semantic) outperforms either method alone on mixed-query corpora — but not on every corpus. This article covers when to add it, how to fuse results with RRF, how to measure improvement, when LangChain's in-memory BM25 breaks, and how reranking fits in.

Re-ranking is a second retrieval stage that adds precision without sacrificing recall. This article covers when it's worth the cost, which rerankers are current in 2026, scale cost math, how to measure real impact on your corpus, and how to build a pipeline that degrades gracefully when the reranker fails.

Query transformation closes the gap between how users ask questions and how documents are written — but most teams apply it too eagerly. This article covers when to transform, which technique fits which failure mode, how to measure improvement, and what it costs at scale.

Metadata filtering is the difference between searching your entire corpus and searching the relevant 2% of it. This article covers when to add it, how to design metadata schemas that survive production, and the failure modes that will burn you silently.

Multi-hop retrieval handles questions that require combining facts from multiple documents — but it costs 3–6× more per query than single-hop and compounds retrieval errors at each step. This article covers when multi-hop is worth it, which of three patterns to use, and how to evaluate and monitor it before trusting it in production.

Knowledge graphs enable relationship queries that vector search cannot answer — but full GraphRAG indexing costs $80–130 per 10K pages. This article covers when to use each variant (full GraphRAG, LazyGraphRAG, DIY KG), cost arithmetic, entity extraction and resolution, failure modes, and how to evaluate before shipping.

Static RAG applies the same retrieval strategy to every query. Agentic RAG puts an LLM in control: it chooses the retrieval strategy, escalates when results are poor, and knows when to give up. This article covers the decision loop, strategy escalation, tiered architecture, production operations, and evaluation.

How to make RAG handle multi-turn conversations: when to add it, how query condensation works under the hood, the LangGraph and legacy chain implementations, context budget math, failure modes, and the production architecture.

Real documents contain tables, images, and diagrams — but most teams over-invest in image processing when tables alone deliver 80% of the value. This article covers three strategies (OCR+summarize, ColPali vision embeddings, vision at query time), how vision models fail on financial data, and a first-30-days runbook for incremental deployment.

Corrective RAG adds document grading and query rewriting to the retrieval loop — if retrieved documents don't answer the question, the system rewrites the query and retrieves again. This article covers when the complexity is justified, the real cost and latency tradeoffs, and how to build, evaluate, and monitor the grading loop in production.

Route queries to the optimal retrieval source — vector store, SQL database, API, or web search. This article covers when routing earns its keep (and when querying all sources is cheaper), production-grade implementation with safe SQL and model tiering, multi-source fusion with reciprocal rank fusion, fallback chains, and the RAG-specific failure modes that classification accuracy alone won't catch.

How to choose between batch, streaming, and CDC ingestion; what ingestion actually costs; and the specific ways production pipelines fail — with a complete reference implementation.

A production-grade RAG evaluation playbook: two-stage failure diagnosis, retrieval metrics, LLM-as-Judge generation scoring, the updated RAGAS v0.4 API, golden dataset construction, CI threshold gates, and online monitoring.

A systematic method for diagnosing RAG failures across five pipeline stages — from low similarity scores through hallucination. Covers observability setup with LangSmith, failure mode diagnosis, and the debugging loop that converts bug reports into regression tests.

Reduce RAG costs in production through measure-first profiling, prompt caching (50-90% savings), semantic caching, multi-provider model tiering, context optimization, and cost monitoring.

Fine-tuning embedding models on domain data can improve retrieval recall by 10-25% — but re-ranking, query expansion, and better chunking solve the same problem with less effort in most cases. This article covers when fine-tuning is the right tool, what it costs, how to build training data and train with the modern SentenceTransformerTrainer API, how to measure improvement, common failure modes, and production deployment.

Matryoshka Representation Learning lets you truncate embedding vectors to any prefix length while preserving retrieval quality — but the actual recall loss depends on your model, your corpus, and your target dimension. This article covers the 2026 MRL model landscape, how to benchmark the dimension tradeoff on your own data, when two-stage retrieval justifies its operational complexity, and how to stack MRL with binary quantization for maximum storage savings.

Three retrieval architectures differ in how much they pre-compute vs compute at query time. This article covers when each architecture wins, how to calculate storage and latency for your corpus, how to measure which one improves your retrieval, and the failure modes specific to each.