AI coding without context. What’s the point?
Blog post from Swimm
Large Language Models (LLMs) have become increasingly popular as AI Coding Assistants, aiding in tasks such as code completion and generation. However, their effectiveness is largely dependent on the context provided by users. Without specific knowledge of a company's codebase, these models rely on generic data, which can lead to unreliable or incorrect code. Providing partial context improves the generated code but still requires human verification to ensure accuracy and account for edge cases. The principle of "Gold In, Gold Out" emphasizes the need for quality input to achieve quality output. The key to maximizing the potential of AI Coding Assistants is delivering precise and relevant context, which ensures the practical and reliable generation of solutions.
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