Cutting AI Token Costs: Why Less Codebase Context Beats More

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A software engineer discovered that small two-file code iterations were consuming around 350,000 tokens each, not due to task complexity but because the executor was autonomously scanning the repository for types, patterns, and helpers before writing any code. The root cause was uncontrolled reconnaissance — nine-tenths of token spend produced nothing in the final diff. The fix was strictly separating task context, which must be complete with exact file paths and signatures, from codebase context, which must be limited only to files explicitly named in the task. Providing broad architectural background as context made things worse, as extra neighbouring code increased the risk of the wrong file becoming an unintended template. The author concluded that vague task definitions paired with generous codebase access is the most common pattern behind failed or expensive AI-assisted coding iterations.

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ProgrammingDEV Community ·

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