Resolved

LLM feedback loop produces no improvement due to vague evaluator feedback

AI/ML
LLM
Prompt Engineering
Feedback Loops
Python
Automation
NLP
Debugging

Multi-turn LLM generation system stuck in GOOD rating loop because evaluator feedback lacks specific actionable details | Root cause analysis and prompt engineering fix for concrete feedback

Evaluator Feedback Too Vague

Issue

The video script generation system using an LLM-based pipeline was stuck in a feedback loop, repeatedly providing vague evaluator feedback such as ‘be more insightful’ or ‘be more focused.’ This resulted in superficial rewrites with no measurable quality improvement.

Culprit

The evaluator feedback mechanism lacked specificity and actionable guidance. It provided abstract requirements without concrete examples, section-specific references, or measurable success criteria. As a result, the LLM could not effectively implement the requested changes, leading to stagnation in content quality.

Solution

The evaluator feedback was restructured to include specific examples of actionable feedback. This included:

  • Concrete examples of what changes were needed and how to implement them.
  • Section-specific references to pinpoint where improvements were required.
  • Before/after comparisons to enable measurable verification of changes.
  • Clear technical context to guide the LLM in generating improved content.

This resulted in more meaningful feedback cycles and measurable quality improvements in the generated content.