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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
Why chat-based interaction patterns cause context contamination and output degradation
Techniques for scoping, maintaining, and recovering context in AI-assisted workflows
How to design context deliberately to align with how LLMs process information
Who Should Attend
Software Developers
Software Architects
Technical Leads
Teams using AI coding agents in daily development work
