Principle:Truera Trulens Feedback Provider Configuration
| Knowledge Sources | |
|---|---|
| Domains | LLM_Evaluation, NLP |
| Last Updated | 2026-02-14 08:00 GMT |
Overview
A configuration pattern that instantiates an LLM-based evaluation provider to serve as the judge for feedback functions in application assessment.
Description
Feedback Provider Configuration establishes the LLM backend that will act as a judge when evaluating application traces. The "LLM-as-a-Judge" paradigm uses a capable language model to assess qualities like relevance, groundedness, and coherence of another model's outputs. The provider wraps an LLM API (such as OpenAI, Azure OpenAI, or Cortex) and exposes pre-built evaluation methods that can be composed into feedback functions.
This principle decouples the evaluation logic from the specific LLM provider, allowing the same feedback functions to be backed by different models. The provider handles:
- API authentication and rate limiting
- Model selection and configuration
- Prompt formatting for evaluation tasks
- Response parsing and score extraction
Usage
Use this principle after initializing a TruLens session and before defining feedback functions. Configure a provider when you need automated quality evaluation of LLM application outputs. Choose the provider based on available API access and desired evaluation quality (larger models generally produce more reliable judgments).
Theoretical Basis
The LLM-as-a-Judge approach leverages the reasoning capabilities of large language models to evaluate other models' outputs. Key theoretical foundations:
- Reference-free evaluation: Unlike traditional NLP metrics (BLEU, ROUGE), LLM judges can assess semantic quality without reference answers
- Rubric-based scoring: The provider formats evaluation as a structured rubric with defined score ranges (typically 0-3), enabling consistent and interpretable ratings
- Chain-of-thought reasoning: Many evaluation methods use CoT prompting to improve judgment quality by requiring the judge to explain its reasoning before scoring
This normalization maps provider-specific score ranges to a [0, 1] interval for cross-metric comparability.