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.
A practical guide to context budgets for AI agents. How to allocate tokens across system prompts, tools, retrieval, history, and a buffer in production.
Token prices fell 280x but enterprise AI spend rose 320%. Poor context architecture drives 60-70% of total AI costs. Here is where the money actually goes.
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.
Context compression reduces AI agent memory usage by 26-54% while preserving task performance. Here's how it works and why bigger context windows aren't the answer.
Prompt caching reduces AI agent API costs by up to 90% and latency by 31%. Here's how it works, where it breaks, and how to implement it right.
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.
ETH Zurich found AI-generated context files hurt agent performance by 3%. Format choice alone swings LLM accuracy by 40%. Here's what the research says.
New research analyzed 3,282 MCP bug reports across GitHub. The patterns reveal a context delivery problem, not a protocol problem. Here's what it means.
A context window is the total text an AI model can process at once. Learn how they work, why size isn't everything, and what actually affects performance.
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.
Prompt engineering is a dead end. Context engineering — designing what information AI models receive — is replacing it. Here's how to start applying it.
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.
Research shows LLMs drop from 95% to 60% accuracy as context grows stale. Here's how context rot degrades AI performance and why bigger windows won't help.