Retrieval & search
Getting the right slice of a corpus in front of an agent: retrieval, chunking, graphs, ranking.
3 answers · 15 articles · 6 definitions
Definitions in this subject
Is RAG still worth it, or should you fine-tune?
Keep the retrieval, and fine-tune only what retrieval cannot fix. Fine-tuning teaches behavior, not facts: models learn new facts from it slowly and hallucinate more as they do. Retrieval still wins on anything that changes, needs citations, or is scoped per user. Fine-tune when the model already knows enough and only the output shape is wrong.
AnswerIs a knowledge graph worth it, or is better RAG enough?
Usually a better retrieval pipeline first. Most pilots that miss links are failing at entity resolution and lexical matching, not at graph topology, and hybrid search with reranking fixes that for a fraction of the cost. Add a graph when your questions genuinely chain facts across systems, and expect roughly ten accuracy points, not a transformation.
AnswerShould you process context at upload or at query time?
Freshness is a refresh-schedule problem, not a placement problem. Process at upload anything the document alone determines, and leave query time for work that depends on the question. When the corpus changes daily, keep that split and reingest only what changed. Nightly full rebuilds are what actually leave the index behind.
ArticleAgentic retrieval techniques that hold up in production
Six agentic retrieval techniques backed by 2026 benchmarks: read enforcement, query decomposition, routing, split routers, verification, and step budgets.
ArticlePruning context made agents more accurate, not just cheaper
Pruning an agent's tool history raised task completion from 71% to 91.6%. What the summary rescued was not content but the agent's place in its own work.
ArticleWhat chunk size should you use for RAG?
Studies disagree on the best chunk size for RAG: one found 100 tokens with zero overlap won. What the research supports, and the bigger lever most guides miss.
ArticleAgentic RAG fails before the reasoning starts
A 12,000-trajectory study finds agentic RAG agents finalize answers without reading retrieved evidence. Forcing one read gains up to 19.9 accuracy points.
ArticleRAG vs long context: what the 2026 data shows
RAG vs long context in 2026: which wins on cost, speed, and accuracy, and when each one beats the other in production. What the benchmarks actually show.
ArticleKnowledge graphs vs RAG: when graphs actually win
Knowledge graphs vs RAG: graph retrieval wins multi-hop reasoning and global summarization, but vector RAG matches it on simple lookups at far lower cost.
ArticleChunking Strategies Decide What Context AI Sees
Chunking strategies decide which context reaches your AI. How fixed-size, semantic, and late chunking change retrieval accuracy, with 2026 benchmark data.
ArticleWhy token cost doesn't scale with knowledge base size
AI token usage scales with knowledge base size only when the full corpus loads per query. The real variable is selective context delivery, not KB size.
ArticleHow to measure context quality for AI agents
Five dimensions of context quality that determine AI agent performance, with metrics, benchmarks, and practical measurement approaches for production systems.
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