Why Doesn’t Anyone Teach Developers About Context Management?

2026-05-14 · Source: AI & ML – Radar · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

Andrew Stellman, in an article published May 14, 2026, argues that context management is a critical, yet often overlooked, skill in AI-driven development. He defines context as "everything the AI is thinking about right now," including prompts, conversation history, and read files, measured in tokens within a finite "context window." When this window fills, AI models lose track, leading to incorrect or strange outputs. Stellman highlights that developers frequently make the mistake of restarting sessions, which causes total amnesia for the AI, resulting in worse code and answers. He cites a Microsoft developer who wasted over an hour daily re-explaining code to AI. The article emphasizes externalizing context to files, such as `DEVELOPMENT_CONTEXT.md`, and including the "why" behind decisions to prevent AI from undoing deliberate choices. This approach, akin to managing memory in code, helps avoid "context drift" and improves AI output quality, as demonstrated by the Quality Playbook and Octobatch projects.

Key takeaway

For AI Engineers and Software Engineers building with AI tools, actively managing context is crucial for high-quality output. You should externalize AI session context to files, documenting project details and the rationale behind design decisions. This practice prevents AI from losing critical information or undoing deliberate choices, ultimately saving time and improving code quality. Consider adopting structured context files to bootstrap new AI sessions effectively and design workflows that leverage externalized state for complex tasks.

Key insights

Effective AI development hinges on explicit context management, preventing AI amnesia and improving output quality.

Principles

• Externalize AI context to files.
• Include reasoning for all decisions.
• Design workflows for independent sessions.

Method

Store development context in files like `DEVELOPMENT_CONTEXT.md`, detailing project scope, build processes, and decisions. Update these files continually, ensuring they reflect changes and include the "why" for each decision.

In practice

• Use `CONTEXT.md` files for project blueprints.
• Document decision rationale to prevent AI refactoring.
• Split long AI sessions into independent phases.

Topics

• Context Management
• AI-driven Development
• Context Window
• Externalized Context
• Agentic Engineering

Code references

• andrewstellman/quality-playbook
• andrewstellman/octobatch

Best for: AI Engineer, Software Engineer, Director of AI/ML

Related on AIssential

• Your AI Agent Already Forgot Half of What You Told It — AI context loss is mitigated by externalizing information to persistent files.
• My cheatsheet for a clean context — Effective coding agent use requires mindful context management, precise prompting, and systematic debugging.
• Your Prompts Aren’t the Problem—Your Context Is — Effective AI performance hinges on managing the model's context window with relevant, timely information, not just prompt wording.
• [404] – Developer Not Found: The Continuing Developer Evolution • Derek Bingham • YOW! 2025 — The developer role is evolving, requiring new skills in context engineering and structured AI interaction to harness agentic coding assistants effectively.
• Using Claude Code: session management and 1M context — Effective context management in Claude Code is crucial for optimizing performance and mitigating "context rot."
• So Long and Thanks for All the Context — The U-shape problem, where LLMs ignore middle context, is a fundamental transformer architecture property requiring explicit management.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI & ML – Radar.