Top 7 Challenges with Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is a powerful framework that combines the strengths of information retrieval and text generation. By leveraging large language models (LLMs) alongside a knowledge base, RAG systems can generate highly informative and contextually relevant responses. The process involves retrieving relevant documents from the knowledge base and using them as context to generate answers to queries. Implementing RAG applications is straightforward, but making them robust, scalable, and highly accurate is a different story. Several significant challenges need to be addressed to optimize the system's performance.

In this blog, we'll dive into some of the most common issues faced when working with RAG systems and discuss potential solutions to overcome them. Our insights are based on the scientific paper "Seven Failure Points When Engineering a Retrieval Augmented Generation System" and the insightful blog article "12 RAG pain points and proposed Solutions" from Towards Data Science. By understanding these challenges, you can better navigate the complexities of RAG and enhance the effectiveness of your applications.

The challenges discussed in this blog are listed as:

1. Missing Content in the Knowledge Base: The LLM provides incorrect answers due to the absence of necessary information in the knowledge base.
2. Difficulty in Extracting the Answer from the Retrieved Context: The LLM fails to extract the correct answer from the context, often due to noise or conflicting information in the retrieved documents.
3. Output in Wrong Format: The output from the LLM doesn't match the desired format, such as tables or lists.
4. Incomplete Outputs: The model returns partially correct answers, missing some relevant information available in the knowledge base.
5. Data Ingestion Scalability: Large volumes of data overwhelm the ingestion pipeline, affecting the system's ability to manage and process data efficiently.
6. Secure Code Execution: Running executable code poses risks, including potential damage to the host server or loss of important data.
7. Working with PDFs: Extracting data from complex PDFs with embedded tables and charts requires sophisticated parsing logic due to inconsistent layouts and formats.