The science and practice of managing, optimizing, and engineering context for AI coding agents.
Without a code graph, AI agents navigate blind and read 15 files to answer a 3-file question. Here's why it happens and how graph-based context selection fixes it.
Your AI coding agent is only as good as the context it receives. Learn how CLAUDE.md files, memory dir, and graph-based retrieval to eliminate re-explanation overhead and boost first-attempt accuracy.
Most AI coding sessions waste 80%+ of tokens on irrelevant context. Here’s why it happens, how it hurts cost and quality, and how dependency graphs fix it.
Learn how AI context windows work, why long coding sessions degrade, and practical strategies and tools like vexp to keep Claude effective and costs low.
Prompt engineering shapes intent; context engineering shapes what the model actually knows. For serious AI coding, context wins by a mile.
Why current AI coding agents forget your codebase between sessions, and how vexp’s session memory, staleness tracking, and cross-agent graph fix it.
AI coding agents waste tokens by loading hundreds of irrelevant files. Dependency graphs fix this with precise, structural context selection.
Why context, not prompts, is the real bottleneck for AI coding agents—and how dependency graphs, session memory, and token budgets fix it.