Large language model (LLM) coding assistants like Sourcegraph Cody, Aider, and Tabby help developers generate and apply code changes. This report examines how these open-source tools prompt LLMs to produce patches, integrate the changes into code, handle common issues, verify results, and what challenges remain.
Structured Prompts for Code Edits – These assistants carefully craft prompts so the LLM knows exactly how to output changes. For example, Aider uses specialized edit formats: it can ask the LLM for a full file rewrite or a diff. Aider often defaults to a diff format, where the LLM is told to return only the changed parts of files using a syntax similar to a unified diff or marked “search/replace” blocks. This reduces token usage and focuses the LLM on the edits. The prompt includes instructions like “produce changes in this format” with file paths and code fences, so the model returns patches instead