Databricks offers a detailed exploration of the evaluation techniques specific to LLM applications in RAG. The emphasis is on the importance of robust metrics and the nuanced intricacies of the evaluation process.


In the ever-evolving world of Natural Language Processing, understanding how to evaluate Language Model applications is paramount. This article, inspired by Databricks’ “Best Practices for LLM Evaluation of RAG Applications”, delves deep into the metrics, methodologies, and challenges of evaluating LLM applications using Retrieval-Augmented Generation (RAG).

Key Metrics for Evaluation

Three primary metrics stand out in the evaluation process:

  1. Correctness: A pivotal metric that focuses on the accuracy of LLM-generated answers, ensuring they are both factually correct and relevant.
  2. Comprehensiveness: This metric assesses the depth and breadth of answers, ensuring a holistic response that covers all aspects of the posed question.
  3. Readability: Beyond just being accurate and comprehensive, answers must be clear and easy to understand. This metric ensures clarity and conciseness.

The combined rating for answers is derived from a weighted score: 60% for Correctness, 20% for Comprehensiveness, and 20% for Readability.

Evaluation Methodologies

Databricks provides a structured approach to the evaluation:

  1. Generate Evaluation Dataset: Curating a dataset from relevant questions and their contexts is the foundational step.
  2. Accuracy through Examples: Emphasizing the need for clear instructions and examples for the LLM judge ensures consistent evaluations.
  3. Use of Low-Precision Grading Scales: A recommendation to use lower-precision grading for consistency.
  4. Dedicated Benchmarks for RAG Applications: Performance on one benchmark doesn’t guarantee similar results on others, highlighting the need for RAG-specific benchmarks.

Challenges in Auto-Evaluation

Several challenges are inherent in the auto-evaluation process:

  1. Alignment with Human Grading: The LLM judge’s grading should closely mirror human preferences.
  2. Optimal Use of Examples: Determining the right number and type of examples for the LLM judge.
  3. Consistency in Grading Scales: With varied grading scales across frameworks, achieving uniformity is challenging.
  4. Versatility of Evaluation Metrics: Ensuring that metrics are applicable across different use cases.

Tools for LLM Evaluation

Several tools and methodologies are highlighted for LLM evaluation:

1. MLflow Evaluation API

Databricks’ advancements in the MLflow Evaluation API include: - MLflow 2.4: Introduction of the Evaluation API for LLMs. - MLflow 2.6: Incorporation of LLM-based metrics like toxicity and perplexity. - Upcoming features include “LLM-as-a-judge” support.

2. Doc_qa Repository

This repository contains the essential code and data for Databricks’ experiments.

3. LLMs as a Judge

The potential of using LLMs, like GPT-4, as judges for automated evaluation is being explored by the LLM community.


The insights from Databricks underscore that while there’s a clear roadmap for LLM application evaluation, the journey is laden with challenges. As the NLP domain progresses, the continuous refinement of these evaluation techniques will be essential.