When long chats drift: hidden errors in AI-assisted coding

The author details their experience with "context drift" in long AI chat sessions, where the model forgets or deprioritizes initial constraints. This drift leads to subtle but impactful errors, such as using incorrect APIs or suggesting incompatible language features. These errors often arise from a series of micro-decisions and the model filling in gaps in responses. Model outputs can feed into other tools, amplifying errors through missing checks or incorrect assumptions. The author emphasizes the need for separate generation and validation steps to mitigate these issues. They implemented operational changes like forcing session resets, comprehensive logging, and mandatory tool output checkpoints. They also use explicit guardrails in prompts to specify runtime, versions, and style guides. The author also uses multiple models and research flows to verify API details and assumptions. Their heuristic is to stop and validate after a few turns or when multiple subsystems are involved. They now treat dependency changes as new projects and require explicit verification and logs for all drafts. The core takeaway is to prevent small context drifts from causing software outages through careful validation.