Source URL: https://www.greptile.com/blog/make-llms-shut-up
Source: Hacker News
Title: How to make LLMs shut up
Feedly Summary: Comments
AI Summary and Description: Yes
**Summary:**
The text discusses the challenges and solutions encountered while developing an AI-powered code review bot, particularly focusing on the issue of excessive and often unhelpful comments generated by large language models (LLMs). The insights underline the importance of refining AI outputs to enhance user experience and productivity in code reviews, making it particularly relevant for professionals engaged in AI and software security.
**Detailed Description:**
The provided text outlines the journey of Daksh, co-founder of Greptile, in enhancing the performance of an AI code review bot. This product aims to streamline the code review process by offering first-pass reviews of pull requests (PRs). However, the initial version faced significant criticism, leading to a series of iterative improvements. Here are the key points:
– **Initial Challenges:**
– The AI code review bot generated too many comments in response to PR changes, leading users to overlook important feedback.
– Identifying which comments were valuable required assessing their quality and impact on the review process.
– **Evaluation Methods:**
– Two primary methods were considered to determine comment quality:
– Feedback through reactions (👍/👎) on GitHub comments.
– Analyzing subsequent code diffs to see which comments were addressed.
– **Findings from Comment Analysis:**
– Of the comments reviewed:
– About 19% were considered useful.
– 2% were incorrect and unhelpful.
– 79% were categorized as ‘nits’—comments that were factually correct but not significant to the developer.
– **Attempts to Improve Comment Quality:**
– **Prompting:** Initial strategies involved prompt engineering to improve the comments generated, but these efforts were largely ineffective.
– **LLM-as-a-judge:** A method where the LLM was tasked with assessing its comment severity also failed due to inconsistency and inefficiency.
– **Critical Learnings:**
– Effective prompting for reducing nits was not achievable.
– LLMs struggled to evaluate their own outputs reliably.
– The subjectivity of nits made them challenging to standardize across different teams.
– **Final Approach: Clustering:**
– A new strategy was implemented involving the creation of embedding vectors for comments based on upvotes and downvotes from developers, stored in a vector database.
– This approach filtered out repetitive nits by comparing the cosine similarity of new comments with downvoted ones.
– The system proved successful, leading to a notable increase in the address rate of comments (from 19% to over 55%).
– **Ongoing Efforts:**
– While the filtering technique yielded substantial improvements, enhancing the effectiveness of the AI bot remains a priority for the team, with plans for further updates in the future.
This narrative is particularly relevant for AI, software security, and compliance professionals as it highlights practical challenges in AI implementation, the importance of iterative improvement in AI products, and the necessity for effective quality control in automated processes.