Troubleshooting RAG-based LLM applications

Large language models (LLMs) like GPT-4, Claude, and Llama are behind popular tools such as intelligent assistants and customer service chatbots. However, these applications often lack proprietary or context-specific knowledge due to the information they were trained on. To address this limitation, organizations integrate retrieval-augmented generation (RAG) into their LLM applications. RAG enhances the accuracy and relevance of responses by retrieving information from external datasets beyond an LLM's preexisting knowledge base. While RAG improves LLMs, developers and AI engineers face challenges such as managing latency, ensuring the relevance of retrieved data, maintaining model accuracy, and handling large volumes of diverse information in real time. To mitigate these issues, strategies like chunking and choosing the right embedding model to reduce latency, implementing hybrid search to limit irrelevant or inaccurate responses, using vector databases to exclude outdated information, and scanning prompts and responses to prevent accidental exposure of sensitive data can be employed.