Evaluation, Iteration, and Testing for Optimal Performance of your LLM apps

To ensure the quality and effectiveness of LLM-based applications, it is crucial to evaluate their performance using feedback functions that measure groundedness, relevance, and toxicity, among other aspects. In this blog post, we will explore the importance of evaluating LLMs and the steps to create a comprehensive evaluation framework using feedback functions.

Evaluate

Evaluating LLMs involves several key steps:

1. Choose appropriate feedback functions: Select feedback functions that are relevant to your use cases, such as groundedness, relevance, toxicity, truthfulness, question-answering relevance, and user sentiment.
2. Leverage built-in feedback functions: Utilize an extensible library of built-in feedback functions to programmatically evaluate the quality of inputs, outputs, and intermediate results.
3. Iterate: Observe where applications have weaknesses to inform iteration on prompts, hyperparameters, and more.
4. Test: Compare different LLM chains on a metrics leaderboard to pick the best-performing one

Iterate

After evaluating your LLM app with various feedback functions, it is essential to iterate and improve its performance:

1. Identify weaknesses: Observe where applications have weaknesses to inform iteration on prompts, hyperparameters, and more.
2. Improve groundedness: Ensure that the application forms accurate answers based on the retrieved context, separating the response into individual claims and independently searching for evidence that supports each within the retrieved context.
3. Enhance answer relevance: Verify that the final response helps fully answer the original question by evaluating its relevance to the user input4.

Test

Comparing different LLM chains on a metrics leaderboard allows you to pick the best-performing one. Some common evaluation metrics include perplexity, BLEU score, ROUGE score, and METEOR score.

• BLEU Score (Bilingual Evaluation Understudy): This metric evaluates the similarity between a translation and a set of reference translations. It considers both the translation and the reference translations when calculating the score, making it suitable for comparing translations
• ROUGE Score (Recall-Oriented Understudy for Gisting): This metric measures the recall, precision, and F1 score of an LLM's output, helping you understand how well the model is performing in terms of its ability to retrieve relevant information
• METEOR Score: This metric evaluates the translation quality of an LLM by comparing its output to a set of reference translations. It considers both the translation and the reference translations when calculating the score, making it suitable for comparing translations

Additionally, human evaluation can provide a more nuanced assessment of meaning and help identify potential issues, such as subtle forms of bias or appropriateness of content in a specific cultural context

The RAG Triad

The RAG (Retrieval-Augmented Generation) triad is a standard for providing LLMs with context to avoid hallucinations. However, even RAGs can suffer from hallucination when retrieval fails to retrieve sufficient context or retrieves irrelevant context that is then woven into the LLM's response

1. Context Relevance: Ensure that each chunk of context is relevant to the input query, as irrelevant information in the context could lead to hallucinations.
2. Groundedness: Verify that the application forms accurate answers based on the retrieved context, separating the response into individual claims and independently searching for evidence that supports each within the retrieved context.
3. Answer Relevance: Evaluate the relevance of the final response to the user input.

By reaching satisfactory evaluations for this triad, you can make a nuanced statement about your application's correctness, stating that your application is verified to be hallucination-free up to the limit of its knowledge base

In conclusion, evaluating and iterating on LLM-based applications using feedback functions is essential for ensuring their quality and effectiveness. By leveraging built-in feedback functions, comparing different LLM chains, and focusing on the RAG triad, you can create a comprehensive evaluation framework that helps you build powerful and reliable LLM.