When to process AI context: ingestion, dreaming, query
There are three moments to process AI context: ingestion, a background pass some call dreaming, and query time. Match each kind of work to the right one.
Further reading
Articles about RAG (Retrieval-Augmented Generation)
14 articles from the Wire blog, sorted newest first. Return to the RAG (Retrieval-Augmented Generation) definition for context.
RAG, long context, or fine-tuning: how to choose
RAG, long context, or fine-tuning? A 2026 decision guide on cost, accuracy, and freshness, with a use-case table for choosing the right one in production.
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.
RAG vs fine-tuning: when to use each
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: 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.
RAG Is Not Enough: When Retrieval Fails Your AI
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.
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