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Context Management for Everyday Software Development
Wed, 22 April
AI coding agents are now widely used by software developers, but they introduce a subtle and often overlooked problem. The chat-style interface encourages human conversational habits that do not align with how large language models process information. This mismatch leads to context contamination, loss of relevant details, drift, bias, and gradual degradation of outputs, all of which can quietly undermine code quality.
This session presents practical, methodical approaches to creating and managing context when working with AI coding agents. It introduces tool-agnostic techniques for scoping context, preventing contamination, maintaining context quality, and recovering when context breaks down. By moving beyond the chat metaphor and treating context as an information architecture problem, teams can shift from reactive troubleshooting to proactive design and achieve more consistent, reliable results from LLMs.
What You Will Learn
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Why chat-based interaction patterns cause context contamination and output degradation
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Techniques for scoping, maintaining, and recovering context in AI-assisted workflows
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How to design context deliberately to align with how LLMs process information
Who Should Attend
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Software Developers
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Software Architects
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Technical Leads
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Teams using AI coding agents in daily development work
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About the speaker
Ragunath Jawahar
Founder, Legacy Code HQ
Ragunath Jawahar is the Founder of Legacy Code HQ, where he specializes in helping developers and organizations master massive, complex codebases. With nearly 15 years in the industry and 5 years working with large codebases across startups and enterprises, he discovered that software complexity is fundamentally a human comprehension problem, not just a technical one.
To solve this challenge, Ragunath has built innovative visualization tools including Eureka and Timelapse (open-sourced on GitHub), which help developers navigate complex systems by surfacing relevant information while filtering out noise. His unique expertise combines legacy codebase rescue with 2+ years of AI-assisted development experience, positioning him to address a critical emerging problem: AI's acceleration of generating hard-to-maintain codebases.
Through his work at Legacy Code HQ, Ragunath teaches developers how to harness generative AI to build production-grade applications while avoiding maintainability pitfalls—leveraging first principles from human cognition, software development, and AI. This rare combination of legacy code mastery and AI expertise makes him uniquely qualified to help teams build maintainable software in the age of AI acceleration.
