How to improve RAG accuracy and performance - Top Problems with RAG systems (and ways to mitigate them)

Background

Retrieval-Augmented Generation (RAG) has become a popular approach in building large language model (LLM) applications. As we saw in the last article in this series, by combining retrieval mechanisms with the generative capabilities of LLMs, RAG systems allow applications to ground outputs in external data sources, enhancing accuracy and relevance of the LLM application.

However, implementation of an optimized RAG system can be challenging. Below, we explore some of the top problems that arise when building and using RAG systems, along with the underlying reasons.

1. Missing Content

A significant challenge in RAG systems arises when the answer to a user query is not present in the indexed documents. In such cases, the system may generate a misleading response or fail to recognize that it lacks the necessary information. For instance, a legal research RAG system queried about a rare legal clause might fabricate a plausible but incorrect response if the indexed documents lack coverage of that clause. The ideal behavior in such scenarios is to acknowledge the gap with a response like, “Sorry, I don’t know,” but many systems fall short, leading to diminished user trust. Additionally, it is critical to fill in the respective gaps in the system so that they incrementally improve over time.

Ways to mitigate?

• Implement robust error messaging to indicate the absence of relevant information explicitly, ensuring transparency and user trust.
• Add missing information into the index periodically
• Introduce ways to halt the LLM answer generation when RAG retrieval scores are too low (if this suits your business use case) as lacking an insightful context may cause your LLM to hallucinat

2. Suboptimal retrieval and ranking

Even when the correct answer exists within the indexed dataset, it may not rank highly enough to be retrieved due to suboptimal ranking algorithms. For example, a healthcare RAG system might fail to rank the most relevant clinical study for a query about a specific treatment, causing less useful documents to dominate the results. This can lead to incomplete or irrelevant responses being generated. Ranking algorithms that rely solely on similarity scores often overlook contextual or domain-specific nuances.

Ways to mitigate?

• Enhance ranking mechanisms by incorporating metadata such as document types, authorship, or publication date for prioritization.
• Experiment with a stronger re ranking model that is able to pick the most relevant content for a given user and a query

3. Context Limitations

Token limits in LLMs pose a significant hurdle when processing large datasets. When many documents are retrieved, the consolidation process may truncate essential information, leading to incomplete or noisy input for the model. For example, an educational RAG system tasked with summarizing course content might exclude key sections of a syllabus if the context exceeds the token limit. This truncation reduces the quality of the generated response.

Ways to mitigate?

• Optimize chunking strategies to create balanced, contextually coherent segments of information.
• Implement context filtering and consolidation techniques to condense and prioritize the most relevant and high-quality information for the LLM to use.

4. Contradicting Information

Contradictory information in the retrieved context can mislead the LLM, resulting in inaccurate or hallucinated outputs. Contradictory or irrelevant data within the context exacerbates this issue. For instance, a customer support RAG system might retrieve outdated policies alongside current ones, causing the LLM to generate a confusing or incorrect response. This is particularly problematic in domains like law or finance, where precision is critical.

Ways to mitigate?

• Refine the consolidation process to filter irrelevant or contradictory information before it reaches the LLM.
• Test and fine-tune prompts to direct the LLM’s attention toward the most relevant / most recent aspects of the context.

5. Incomplete Answers

Incomplete answers occur when the system fails to synthesize all relevant information, even when it is present in the retrieved context. For example, a legal RAG system asked to summarize the key points of three different cases may only address one or two, leaving out critical details from the third.

Ways to mitigate?

• Test and refine chunking methods to ensure each segment includes complete, coherent information.
• Use hierarchical retrieval strategies to fetch additional context as needed, improving response completeness.

6. Performance and Scalability problems in retrieval

Performance and scalability challenges in Retrieval-Augmented Generation (RAG) systems arise when the retrieval component struggles to handle large-scale datasets or high query volumes efficiently. As the corpus size grows, retrieval latency increases due to computational overhead in searching, ranking, and retrieving relevant documents. Additionally, real-time applications may face bottlenecks in handling concurrent queries, resulting in delayed responses. These problems are further compounded by the resource-intensive nature of embedding generation and updates, as well as limitations in system architecture that fail to scale horizontally.

Ways to mitigate?

To handle large-scale datasets efficiently, robust and scalable solutions are necessary. Here are key strategies to mitigate performance and scalability issues:

• Distributed Architecture: Scale horizontally by distributing data and workload across nodes to handle high query volumes and ensure resilience.
• Optimized Indexing: Use advanced indexing methods (e.g., IVF_FLAT, HNSW, DiskANN) for faster searches and cost-efficient scalability.
• Metadata Filtering: Enable attribute-based filtering to improve search relevance and minimize post-processing.
• Hardware Optimization: Utilize CPUs for flexibility and GPUs for accelerated embedding computation and similarity searches.
• Caching: Cache frequently queried embeddings or results to reduce computation and improve response times.

Impact of an inefficient RAG system

RAG is a crucial component of many LLM applications, significantly enhancing the accuracy of LLM responses. An inefficient RAG system can lead to issues such as hallucinations in outputs and a poor user experience, ultimately resulting in negative business impacts. We strongly recommend adopting best practices for building robust RAG systems. Stay tuned for our next article, where we’ll explore these best practices in detail.

Conclusion

Issues such as missing content, poor document ranking, context limitations, and noise in retrieved information highlight the complexity of designing robust and reliable systems. Addressing these problems requires a combination of thoughtful system architecture, rigorous testing, and continuous optimization. In the next article in this series, we will explore best practices in the areas of ideal document chunking, embedding strategies, retrieval algorithms, and consolidation techniques. As the technology evolves, implementing these best practices will be crucial to unlocking the full potential of RAG systems in fields ranging from education to healthcare, ensuring they deliver on their promise of accurate, contextually rich, and user-aligned responses.