Definition
Context Engineering: The practice of deliberately designing, structuring, and managing the information provided to AI models to improve output quality and relevance.
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.
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.
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.
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.
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.
AI customer service fails at 4x the rate of other AI tasks. Support bots need five types of context most teams never provide. The model isn't the problem.
65% of agent failures come from context drift, not token limits. Here's how context compression keeps long-running AI agents on track.
AI agent memory fails because it's a context engineering problem, not a storage problem. Research reveals three failure modes and what actually works.
84% of developers use AI coding tools, but only 29% trust the output. The problem has less to do with models and more to do with codebase context.
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.
84% of product teams doubt their products will succeed despite AI adoption. The problem: PM tools see feature requests but not the context behind what to build.
87% of enterprises missed revenue targets despite AI investment. Sales AI needs five types of deal context most teams never provide. The model isn't the issue.
Up to 86.7% of multi-agent AI runs fail. Most failures trace back to how agents share context, not the agents themselves. Here's why and how to fix it.
Seven context engineering techniques used in production AI systems, with implementation patterns, research backing, and guidance on when each one works.
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.
88% of organizations report AI agent security incidents. The root cause is a context engineering failure: agents get all-or-nothing access, not scoped context.
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.