Evaluating Large Language Models: An Insightful Process

Evaluating a Large Language Model (LLM) or choosing a Retrieval-Augmented Generation (RAG) technique might seem like an overwhelming task. However, breaking it down into a series of systematic steps can ease the process substantially. Here, I will walk you through a structured approach to ensure that your application truly benefits from the enhancements rather than relying on intuition.

1. Define Your Metrics Relevance

It's crucial to start by clarifying which metrics are most important to your application's needs. Quality, cost, and latency are common contenders. Pinpoint exactly what measures of performance you aim to optimize.

2. Create a Reference Dataset

The next move involves assembling a dataset that embodies your typical use scenarios, complete with expected inputs and outputs. It might include the data fed into the entire application or just the inputs directed to the LLM, including any RAG documents. Consider enriching the dataset with multiple example outputs if it helps in drawing clearer comparisons.

3. Create a Metrics Runner

Utilizing a tool such as benchllm can be invaluable for measuring your application's metrics. Employ GPT-4-Turbo to evaluate responses against your reference dataset. While GPT-4o is another option, my personal experience suggests it doesn't fare as well with evaluation tasks.

4. Manually Review Results and Improve Reference Dataset

Delve into the results by reviewing them manually. This does not only aid in optimizing your reference answers but also in pinpointing false positives and false negatives that could skew your evaluation.

5. Measure a Baseline

Understanding where you currently stand is vital. Establish a baseline by noting down the existing metrics with your current setup.

6. Switch and Measure Delta

Experiment by trying a different technique or LLM, or even using a service like airouter.io. Assessing how these changes impact your metrics will shed light on the effectiveness of your modifications.

By following these steps diligently, you pave the path towards genuine improvements in your application's performance, steering clear of relying purely on gut feeling.

A Systematic Approach to Evaluating Large Language Models

This guide outlines a structured approach to assess Large Language Models and Retrieval-Augmented Generation techniques, emphasizing the importance of defining metrics, using reference datasets, and measuring performance improvements through a systematic process.