On Retrieval Augmented Generation. Q&A with Neil Kanungo

Q1. What is Retrieval Augmented Generation (RAG)? 

Retrieval Augmented Generation (RAG) is a method that combines the capabilities of generative models, like LLMs, with information retrieval techniques. In a RAG system, when presented with a query, the model retrieves relevant information from a vast database of documents or data points. It then uses this retrieved information to generate a response. This approach allows RAG to provide more accurate, context-rich, and up-to-date responses, as it leverages both the generative power of LLMs and the precision of external data sources. 

Q2. Are current retrieval methods sufficient to make Large Language Models (LLMs) a reliable component in business processes? 

Current retrieval methods, while advanced, still have limitations. They are typically good at fetching relevant information based on keywords or semantic queries but may struggle with more nuanced or contextually complex inquiries. For LLMs to be reliably integrated into business processes, these retrieval methods must be continually refined to better understand and interpret the intricacies of human language and context. However, in many cases, especially for straightforward informational queries or data analysis tasks, they are sufficiently reliable. 

Q3. Two problems of LLMs are that the training data tends to be out-of-date and they extrapolate when facts aren’t available. Do you agree with this? What are the consequences of these problems in practice? 

Yes, I agree with these issues. LLMs are trained on datasets that may not include the most current information, leading to responses that are out-of-date. Additionally, when LLMs encounter gaps in their training data, they might extrapolate or generate responses based on patterns learned during training, which can result in inaccuracies. In practice, this can lead to outdated or misleading information, which might affect decision-making processes, customer interactions, or any application relying on current and accurate data. 

Q4. Can RAG help address both of these issues? If yes, how? 

RAG can indeed help address these issues. By integrating real-time data retrieval into the response generation process, RAG systems can access the most current information available, mitigating the issue of out-of-datedness. For gaps in knowledge, instead of relying solely on extrapolation, RAG systems can pull in relevant, real-time data from external sources, leading to more accurate and fact-based responses. 

Q5. Who is going to benefit from RAG and who is not? 

RAG is particularly beneficial for organizations that rely on up-to-date information and knowledge-intensive tasks, such as finance, healthcare, legal, and research entities. It’s also useful for customer service applications where accurate, current responses are critical. However, RAG might be less beneficial for entities with limited access to comprehensive and current databases, as the effectiveness of RAG is tied to the quality and relevance of the data it can retrieve. 

Q6. Can you give us a simplified example of using RAG with LLMs? 

Imagine a financial analyst using an LLM-powered tool to query current market trends. Without RAG, the LLM might generate a response based solely on its training, which could be outdated. With RAG, the LLM can pull in the latest market data from various financial databases, ensuring the analyst receives the most current information integrated with insightful analysis. 

Qx. Anything else you wish to add? 

It’s important to note that while RAG significantly enhances the capabilities of LLMs, it is not a silver bullet. The quality of the retrieved data and the integration mechanism between the retrieval system and the generative model are crucial for optimal performance. Continuous monitoring and updating of both the LLM and the data sources are essential to maintain the efficacy and accuracy of RAG systems. 


Implementing RAG with KDB.AI and LangChain


Neil KanungoVP of Product Led Growth, KX

Sponsored by KX

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