
Context Window Management for AI Coding: The Developer's Guide
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.
Technical articles about AI coding agents, context engineering, and developer productivity.

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.

A practical 2026 comparison of GitHub Copilot, Cursor, and Claude Code based on real production use, with a focus on context, agentic workflows, and pricing.

Use a dependency-graph MCP server (vexp) to feed Claude Code only structurally relevant context and cut token costs by ~58%—no prompt changes required.

Prompt engineering shapes intent; context engineering shapes what the model actually knows. For serious AI coding, context wins by a mile.

A controlled benchmark on a real FastAPI app shows dependency-graph context cuts Claude Code costs by 58%, speeds tasks up 22%, and shrinks output tokens 63%.

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.