Implement a contextual summarization approach that enhances accuracy over time using structured feedback, without relying on RLHF.

Ensure strict data filtering to prevent exposure of sensitive details.

Allow users to customize summaries (e.g., adjust detail level, highlight critical changes).

Instead of severity-based filtering, the bot will comment with a severity score, helping users quickly identify critical issues.

Provide explanations with references, helping users understand why a suggestion was made.

Optimize performance by using incremental analysis, reviewing only modified lines instead of the entire file.

Introduce a review step before committing, where users can accept or reject changes.

Introduce an opt-in/opt-out feature to allow users control over data retention while ensuring compliance with SOC2 standards.

Implement conflict detection mechanisms, alerting users to potential merge issues before committing.

Use a Customizable Agent approach instead of model training, enhancing responses through prompt engineering and additional context.

Enable user feedback loops, allowing developers to rate and improve responses.

Optimize AI query processing for speed, reducing response time.

Implement smart search and tagging, making information retrieval intuitive.

Use automated version tracking to keep documentation up-to-date.

Enforce role-based access control (RBAC) to ensure secure information sharing.