Agentic 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.
Further reading
Articles about RAG (Retrieval-Augmented Generation)
15 articles from the Wire blog, sorted newest first. Return to the RAG (Retrieval-Augmented Generation) definition for context.
What 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.
Agentic 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.
Knowledge 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.
Chunking 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.
Why AI customer support replies sound generic
AI support replies sound generic because teams treat brand voice as a prompt problem. Context engineering fixes it by selecting the right exemplars.
Why 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.
Context Poisoning: When Bad Data Becomes AI Ground Truth
Context poisoning plants false data into an AI agent's memory or RAG index. The model treats it as truth. It's a context engineering problem, not a model bug.
RAG 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.
How context engineering reduces AI hallucinations
Most AI inaccuracies in production are context quality failures, not model fabrications. Here's the research on what context engineering actually changes.
How to connect AI to private data safely
77% of employees share sensitive data with AI tools. Five context engineering patterns give AI what it needs without exposing what it shouldn't see.
How 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.
Why Semantic Search Beats Keywords for AI
Hybrid search improves AI retrieval accuracy by up to 41% in technical domains. Here's how semantic search works, where keywords fail, and when you need both.
7 context engineering techniques for production
Seven context engineering techniques used in production AI systems, with implementation patterns, research backing, and guidance on when each one works.
Why AI Hallucinations Are a Context Problem
GPT-5.2 hallucinates at 10.8%, o3-pro at 23.3%. The fix has less to do with better models and more to do with context engineering. Here's the research.
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