ETMI5: Explain to Me in 5

In this section of the content, we dive deep into the evaluation techniques applied to LLMs, focusing on two dimensions- pipeline and model evaluations. We examine how prompts are assessed for their effectiveness, leveraging tools like Prompt Registry and Playground. Additionally, we explore the importance of evaluating the quality of retrieved documents in RAG pipelines, utilizing metrics such as Context Precision and Relevancy. We then discuss the relevance metrics used to gauge response pertinence, including Perplexity and Human Evaluation, along with specialized RAG-specific metrics like Faithfulness and Answer Relevance. Additionally, we emphasize the significance of alignment metrics in ensuring LLMs adhere to human standards, covering dimensions such as Truthfulness and Safety. Lastly, we highlight the role of task-specific benchmarks like GLUE and SQuAD in assessing LLM performance across diverse real-world applications.

Evaluating Large Language Models (Dimensions)

Understanding whether LLMs meet our specific needs is crucial. We must establish clear metrics to gauge the value added by LLM applications. When we refer to "LLM evaluation" in this section, we encompass assessing the entire pipeline, including the LLM itself, all input sources, and the content processed by it. This includes the prompts used for the LLM and, in the case of RAG use-cases, the quality of retrieved documents. To evaluate systems effectively, we'll break down LLM evaluation into dimensions:

1. Pipeline Evaluation: Assessing the effectiveness of individual components within the LLM pipeline, including prompts and retrieved documents.
2. Model Evaluation: Evaluating the performance of the LLM model itself, focusing on the quality and relevance of its generated output.

Now we’ll dig deeper into each of these two dimensions

LLM Pipeline Evaluation

In this section, we’ll look at 2 types of evaluation:

1. Evaluating Prompts: Given the significant impact prompts have on the output of LLM pipelines, we will delve into various methods for assessing and experimenting with prompts.
2. Evaluating the Retrieval Pipeline: Essential for LLM pipelines incorporating RAG, this involves retrieving the top-k documents to assess the LLM's performance.

Evaluating Prompts

The effectiveness of prompts can be evaluated by experimenting with various prompts and observing the changes in LLM performance. This process is facilitated by prompt testing frameworks, which generally include:

• Prompt Registry: A space for users to list prompts they wish to evaluate on the LLM.
• Prompt Playground: A feature to experiment with different prompts, observe the responses generated, and log them. This function calls the LLM API to get responses.
• Evaluation: A section with a user-defined function for evaluating how various prompts perform.
• Analytics and Logging: Features providing additional information such as logging and resource usage, aiding in the selection of the most effective prompts.

Commonly used tools for prompt testing include Promptfoo, PromptLayer, and others.

Automatic Prompt Generation