Generative AI like Cody uses Large Language Models to complete code based on current context, enhancing developer productivity. The process involves planning, retrieving relevant context, generating completions with the LLM, and post-processing. Cody’s efficiency and accuracy depend on factors like understanding single-line vs. multi-line requests, syntactic triggers, and quality of prompts for the LLM. Improvements in latency include optimizing token limits, implementing streaming responses, and reusing TCP connections. Despite challenges such as fill-in-the-middle support and prompt quality, Cody continues to refine the autocomplete process for a seamless user experience. The complexity and sophistication behind the scenes make Cody a powerful AI assistant for developers.
https://sourcegraph.com/blog/the-lifecycle-of-a-code-ai-completion