How to Evaluate a RAG Application (and Avoid Hallucinations in Your LLM)

Hallucinations in large language models (LLMs) can be frustrating and, in some cases, problematic. If your app relies on Retrieval-Augmented Generation (RAG), the focus should be on delivering accurate, grounded answers. Here's a streamlined guide to evaluating your RAG app effectively and avoiding hallucinations.

What’s RAG?

RAG combines three essential steps to produce fact-based, reliable answers:

• Retrieval: Pulling relevant information from a vector database, which stores processed documents (e.g., PDFs) as embeddings for quick searches.

• Augmentation: Combining the retrieved data with the user’s query to provide context.

• Generation: Using the augmented input, the LLM crafts a clear and human-friendly response.

The RAG Triad for Evaluation

Evaluating your app across the RAG Triad—Context Relevance, Groundedness, and Answer Relevance—is key to eliminating hallucinations.

1. Context Relevance

Ensure that the retrieved data is relevant to the user’s query. Irrelevant data introduces noise, leading to inaccurate responses.Example:For the query "Who is SquareShift?", the app should retrieve snippets specifically about SquareShift and not unrelated companies.

2. Groundedness

Check whether the LLM’s response strictly adheres to the retrieved data. Any fabricated or unsupported additions reduce groundedness.Example:If the retrieved data says "SquareShift specializes in workflow automation" and the LLM adds "and cloud solutions" without basis, points should be deducted.

3. Answer Relevance

Finally, ensure the response addresses the user’s actual question, even if it is factually correct.Example:The response "SquareShift is a software company helping automate workflows" directly answers "Who is SquareShift?", making it relevant.

Why Evaluation Matters

Evaluating your RAG application ensures it retrieves accurate data, generates grounded responses, and answers queries effectively. This process minimizes hallucinations and builds user trust.

Emerging Trends in LLM Evaluation

The field of LLM evaluation is evolving rapidly, with emerging trends aimed at enhancing reliability and accuracy:

1. Feedback-Driven Iteration: New evaluation frameworks use user and system feedback loops to refine responses continuously, ensuring that LLMs adapt to specific use cases.

2. Explainability Metrics: Modern tools are focusing on explainability, allowing developers to understand why an LLM generated a particular response.

3. Multimodal Integration: As LLMs begin to support multimodal inputs (text, image, video), evaluation tools are adapting to measure groundedness and relevance across modalities.

4. Real-Time Monitoring: Observability tools like Phoenix are leveraging real-time monitoring to spot and address hallucinations dynamically.

How Tools Are Adopting These Trends

• Tools such as Ragas and TruLens are integrating these trends into their frameworks. For instance, TruLens incorporates feedback functions to align LLM outputs with user expectations, while Phoenix’s real-time insights make it easier to detect and mitigate hallucinations in production.

Conclusion

By focusing on the RAG Triad—Context Relevance, Groundedness, and Answer Relevance—you can fine-tune your RAG application for optimal reliability. Tools like TruLens and Phoenix offer deeper insights into LLM evaluation, allowing you to assess and improve your app with greater precision. These tools integrate emerging trends like feedback-driven iteration, explainability, and real-time monitoring, ensuring your RAG system stays ahead in providing grounded and reliable responses.

In the next steps, we’ll explore how TruLens can make the evaluation process even clearer and more actionable.