Practical context engineering for AI agents: how to structure, deliver, and manage the context that determines whether AI systems actually work.
Research-backed articles on context architecture, retrieval, agent security, and making AI tools more effective. Written by Jitpal Kocher.
Popular topics
88% of organizations report AI agent security incidents. The root cause is a context engineering failure: agents get all-or-nothing access instead of scoped context.
GPT-5.2 hallucinate at 10.8%, o3-pro at 23.3%. The fix has less to do with better models and more to do with better context engineering.
Claude now imports ChatGPT memories, but conversation history, files, and team context don't transfer. Here's what actually moves and what doesn't.
Prompt engineering is a dead end. Context engineering is the discipline replacing it. Here's what it is, why it matters, and how to apply it.
94% of IT leaders fear vendor lock-in. Every AI tool traps your context in its own silo. Here's why, and what's changing.
AI doesn't forget because it's broken. It forgets because everything gets crammed into one place. Here's the technical explanation and how to fix it.
From copy-paste to context platforms, compare five approaches to connecting your specific data to AI tools, with trade-offs for each.
There are 17,000+ MCP servers but most are generic dev tools. Here's how to create one for your own data without writing a single line of code.
76% of enterprises suffer from disconnected AI. Your tools don't share context, and it's tanking performance. Here's what unified context looks like.
RAG is a context-building strategy, not magic. Research shows 70% of retrieved passages miss the mark. Learn why naive retrieval produces poor context and what actually works.
Research shows LLMs drop from 95% to 60% accuracy as context grows. Learn about context rot, the lost-in-the-middle problem, and why bigger context windows aren't the solution.