RAG
15 articles in this topic.
RAG poisoning is an evidence-integrity problem
A retrieval system can reason perfectly from corrupted evidence. Defending RAG means governing what enters the corpus, preserving provenance, isolating tenants, and treating retrieved text as untrusted data.
RAG in one diagram: retrieve, augment, generate
The cleanest mental model for retrieval-augmented generation: why pasting your docs into a prompt beats hoping the LLM already knows them, and the two failure modes that kill every RAG system in production.
When not to use RAG
RAG is the default answer to 'how do I give my LLM custom knowledge?' — and for a wide class of problems, it's the wrong answer. Long-context prompt caching, fine-tuning, and in-context learning each beat RAG in regimes where the costs and assumptions actually pencil out. Here's the decision tree.
Self-RAG and Corrective RAG, the loops that actually help
Two papers from 2023 argued RAG should be reflective: retrieve, judge, re-query if the evidence is bad. By 2026 the practitioner verdict is in — Self-RAG and CRAG actually do help, but only on the queries that need them, and the latency cost is real.
Late interaction, or why ColBERT keeps coming back
Single-vector dense retrieval crushes one nuance per chunk into one point in space. Late-interaction models like ColBERT keep a vector per token and decide similarity at query time. The math is beautiful. The storage cost is brutal. Here's when it's worth it.
Hybrid search, when one retriever isn't enough
Pure vector search misses exact identifiers. Pure BM25 misses paraphrases. Every serious production RAG stack — Notion, Perplexity, Vespa, Glean — runs both and fuses the results. Here's the math, the latency budget, and the production knobs that matter.
Contextual Retrieval, two years on
Anthropic's September 2024 trick — prepend a sentence of context to each chunk before embedding — quietly became the new RAG default. Here's why a 49% retrieval-error reduction held up, what the numbers actually say, and when you still shouldn't bother.
Embeddings in 2026: Voyage, Cohere v4, OpenAI text-embedding-3, mxbai, BGE
The embedding model market split clean. Proprietary models (Voyage 3, Cohere v4, OpenAI v3) own the quality top; open-source (mxbai, BGE-M3, Jina v3, E5) own cost and self-host. Matryoshka rewrote the cost economics. Here's how to actually choose.
Reranking at scale: cross-encoders, ColBERT, mxbai
Retrieve-then-rerank is the single biggest accuracy improvement in production RAG. Three approaches dominate — cross-encoders, late interaction, API rerankers. Here's what each costs, what each wins, and the latency budget that decides between them.
Multi-hop RAG without the hallucinations
Single-hop RAG works until your users start asking questions that require synthesis. Naive multi-hop hallucinates intermediate facts. Here's the working architecture — explicit decomposition, iterative retrieval, self-verification — with the production examples from Harvey and Hebbia that actually ship it.
GraphRAG: when knowledge graphs beat vector search
Microsoft's GraphRAG paper showed that building a knowledge graph from documents and querying community summaries beats vector RAG on global questions. Two years on, the receipts are mixed — the technique works, but the indexing bill is what most teams remember.
Agentic RAG: when the agent decides whether to retrieve
Naive RAG retrieves on every query. Agentic RAG turns retrieval into a tool the agent calls when the answer isn't already obvious — and the production wins are about latency and accuracy at once, not either alone.
Long-context vs RAG: the 2026 verdict
Gemini's 2M-token context, GPT-5's million, Claude's 200K. The argument that long context would kill RAG turned out wrong — but for reasons more interesting than 'cost is high'. Here's where the equilibrium actually settled.
PDF parsing remains unsolved: LlamaParse, Reducto, Unstructured, Marker
Two years into the production RAG era, the single biggest blocker for most enterprise deployments isn't the LLM, the vector store, or the retrieval algorithm. It's whether your PDF parser got the table right. The vendors have multiplied; the problem hasn't been solved.
RAG evaluation in production: Ragas, Phoenix, Braintrust
RAG evaluation is harder than LLM evaluation. Retrieval and generation fail for different reasons and need different metrics. Two years after Ragas formalized the four-metric core, the tooling has split into three camps — here's what each one actually buys you.