Definition
RAG (Retrieval-Augmented Generation): A technique that retrieves relevant documents or data at inference time and injects them into the model's context window before generating a response.
RAG vs fine-tuning: RAG wins for knowledge injection and freshness, fine-tuning wins for style and format. The right choice is a context engineering call.
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.
Long context windows haven't replaced RAG. New 2026 benchmarks reveal the cost, speed, and accuracy tradeoffs, and when each approach wins in production.
Most AI inaccuracies in production are context quality failures, not model fabrications. Here's the research on what context engineering actually changes.
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.
Five dimensions of context quality that determine AI agent performance, with metrics, benchmarks, and practical measurement approaches for production systems.
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.
Seven context engineering techniques used in production AI systems, with implementation patterns, research backing, and guidance on when each one works.
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.
RAG is a context-building strategy, not magic. Research shows 70% of retrieved passages miss the mark. Here's why naive retrieval fails and what works.