Large language models (LLMs) like GPT-4 have transformed natural language processing by generating human-like responses across a wide range of topics. While these models are incredibly powerful, their results can sometimes vary in quality. They can produce remarkably accurate and insightful responses, but at times their output can seem random, irrelevant, or entirely fabricated.
This variability comes from their reliance on patterns learned from large datasets, which may not always provide the specific or most current information needed for certain tasks.
To address these inconsistencies, Retrieval-Augmented Generation (RAG) has emerged as a valuable technique. RAG enhances LLMs by integrating them with external knowledge sources, such as vector databases or specialized information repositories. This approach helps ensure that outputs are not only contextually relevant but also grounded in factual data.
However, implementing RAG-based systems comes with challenges, particularly in assessing the quality of generated responses. The success of an RAG application hinges on how well it retrieves and integrates external information, making thorough evaluation essential. Key aspects to monitor include the relevance of retrieved documents, the accuracy of answers, and the consistency of responses over time.
Evaluating RAG-based systems presents several challenges, primarily because of the complexity of integrating external, potentially dynamic, data sources with LLMs. Here are the key challenges in detail:
One challenge you might face is in the retrieval stage. How do you know that your model is retrieving the right information in the first place?
Note that the embedding model plays a key role in this process. It's responsible for accurately representing textual information as vectors, which are then used to retrieve relevant documents.
The challenge here is to assess how well the embedding model captures the meanings of both the query and the documents. Poor embeddings can lead to irrelevant or misleading document retrieval, which can diminish the quality of the responses generated.
When you begin with a large amount of contextual information—sometimes hundreds of thousands of characters—it's essential to narrow it down to something more manageable, typically around 1,000 characters. While large language models (LLMs) can technically handle extensive inputs, they often focus on the beginning and end of the context window.
This makes it challenging to ensure the most relevant information is retained and used effectively. To evaluate how well an RAG system filters and refines this context, you'll need to find the right balance between keeping essential details and cutting out unnecessary information.
Cosine similarity is often used to assess the relevance of documents, but while it’s efficient, it doesn’t always capture the nuanced relevance of a document to a query. This raises a key challenge: evaluating whether your chosen similarity metric is actually leading to the retrieval of the most contextually appropriate documents.
Be aware that cosine similarity can sometimes over-prioritize documents that appear superficially similar to the query while missing those that might offer deeper, more relevant insights.
A major evaluation challenge is ensuring the faithfulness of generated responses—whether the response is factually correct based on the retrieved documents. It’s important not only to check the accuracy of the response but also to evaluate how well the system links its answers to specific parts of the retrieved documents.
For instance, the system might need to verify a Python version requirement against documentation. Here, it’s essential that the system explicitly cites the sources that support each claim. Evaluating faithfulness means scrutinizing these links to ensure the generated content is directly supported by the retrieved evidence.
Another crucial metric in evaluating RAG-based systems is answer relevancy—whether the generated answer directly addresses your query without unnecessary digressions. For example, there might be times when the system includes extra context, such as information about setting up Label Studio, that, while useful, might not be directly relevant to a specific question about installation. Here, the challenge is to evaluate how well the system avoids irrelevant content while still providing comprehensive answers.
For the purposes of our upcoming tutorial on how to evaluate an enterprise RAG Q&A system, these are the two metrics we’ll be looking at. However, Ragas, a framework for evaluating RAG pipelines, lists many other metrics that may be useful depending on your use case. You can see the full list here.
Integrating information from multiple sources can be particularly complicated, especially when these sources present conflicting information.
For example, different documents might specify different versions of Python as requirements. This inconsistency complicates the evaluation process because the system needs to recognize and either resolve or highlight these conflicts.
You’ll need to assess the system’s ability to manage such conflicts and how well it alerts you to discrepancies in the source material.
One possible way to address this is to use previously generated answers as input for further queries and create a feedback loop, but this can complicate evaluation. Errors or biases in early responses can propagate and amplify in subsequent answers. To evaluate the robustness of the system, you'll need to see how well it handles these iterative processes and whether it can maintain consistency over multiple rounds of question-answering.
Evaluating the way that you evaluate your RAG system can also introduce complexity and challenges.
Using a separate LLM to evaluate the responses generated by the RAG system can help assess answer quality. However, this introduces a layer of subjectivity, as different LLMs might apply varying criteria or interpretations when judging answers.
The challenge is to standardize these evaluations to ensure consistency and reliability. Additionally, the evaluation LLM must be sophisticated enough (think GPT-4 level) to accurately assess the faithfulness and relevancy of the answers.
Human oversight is another issue.
While the RAG system is designed to automate much of the response generation and evaluation, human oversight is invaluable. You might need to refine prompts, correct errors, and validate the system’s outputs.
This human-in-the-loop approach, while beneficial, introduces variability based on the expertise and judgment of the reviewers. You’ll need to evaluate how effectively the system integrates human feedback and how dependent it is on such feedback.
There’s always a trade-off between the depth of evaluation and the time/cost involved. Generating responses, evaluating them for faithfulness and relevancy, and applying metrics like those from Ragas is computationally intensive and time-consuming.
The issue here is to evaluate whether the benefits of thorough evaluation justify the costs, especially in real-time or large-scale deployments.
Moreover, the RAG system allows for both batch processing and interactive evaluation, each with its own trade-offs. Batch processing is faster and more scalable but lacks the nuance and adaptability of interactive, human-in-the-loop evaluation.
You’ll need to assess the system’s performance across these different modes by evaluating its flexibility, speed, and output quality in various operational contexts.
When you evaluate RAG-based systems, you'll find it can be quite computationally demanding. You need to process large amounts of data, generate embeddings, retrieve and rank documents, and then generate and evaluate responses—all of which take up significant time and computational resources.
This becomes even more challenging if you're dealing with large-scale deployments, where thousands of queries might need processing.
You should also consider the financial aspect. Using APIs like OpenAI's and the infrastructure required to run RAG systems can be costly. If you frequently query and evaluate, particularly with large datasets, expenses can quickly add up.
In a followup post, we’ll provide a detailed, step-by-step tutorial on building, evaluating, and improving RAG applications using Label Studio, GPT-4, and Ragas. Stay tuned!
Explore the topic of evaluation for LLMs, its importance, and how we should approach it. Learn how integrating systematic evaluations can help teams iteratively refine their models to meet real-world needs.
In this tutorial, we'll guide you through the process of setting up and using Label Studio in combination with Ragas (Retrieval-Augmented Generation Answer Scoring) and GPT-4 to build an optimized QA application.
Delve into three effective methods to automate your labeling using Label Studio, including examples and resources.
