AI Coding Assistants Need More Context: Experiments and Insights
2025-02-10
Traditional AI coding assistants, while proficient in code generation, often lack crucial context about the broader system environment. This leads developers to spend extra time bridging the gap between code and various information sources. This article details experiments integrating operational context (call graphs, metrics, exception reports) into AI assistants to improve debugging accuracy. Results show structured performance data and error reports enhance AI analysis, but efficiently representing vast amounts of context remains a challenge. The future lies in a knowledge graph encompassing production behavior, system metrics, and more, enabling AI assistants to understand system behavior holistically.