Hi team,
Lately, I’ve been experimenting with building automated code review workflows using LangChain components. A very persistent pain point in production is that LLMs frequently leave comments on lines that don’t actually exist in the patch, or the coordinates drift off by a few lines.
From what I’ve observed, this isn’t just a prompt engineering issue; it’s a structural mismatch between autoregressive token processing and stateful unified diff parsing (especially when tracking @@ -lines,count +lines,count @@ headers over a large context window).
To address this deterministically, I implemented a straightforward validation step that parses the raw unified diff hunks, extracts the valid physical line mapping, and cross-checks/filters the model’s output coordinates before they hit GitHub. It integrates naturally within a RunnableLambda pipeline.
Since AI-driven code review is a very common use case for developers adopting LangChain, I’d love to contribute a clean, end-to-end example to the examples/ directory showing how to combine LangChain components with a deterministic verification layer to handle this line-shifting edge case.
Let me know if this fits the repository’s example roadmap and if you’d be open to a PR. I’m happy to refine the code to match your contribution guidelines.
Best,