Contextualizing AI: Natural language
Blog post from Swimm
AI coding assistants benefit from integrating contextual sources like issues, pull requests, commits, and internal knowledge bases, which provide insights beyond the code itself and help clarify business logic, technical reasoning, and code evolution. This contextual information, often written in natural language, can aid in understanding the motivations behind code changes and the development process but presents challenges like ensuring relevance and accuracy, as outdated information can lead to misleading AI outputs. Internal tools such as Jira and Slack also serve as valuable sources, offering detailed project insights and informal knowledge sharing; however, they require careful navigation to extract relevant information due to their separation from the codebase. Despite these challenges, leveraging such rich context can significantly enhance the performance of AI coding assistants, although it demands meticulous validation and cross-referencing to maintain accuracy and relevance.
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