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What is Retrieval-Augmented Generation (RAG)?

RAG is an architectural pattern in generative AI designed to enhance the accuracy and relevance of responses generated by Large Language Models (LLMs). It works by retrieving external data from a vector database at the time a prompt is issued. This approach helps prevent hallucinations, which are inaccuracies or fabrications that LLMs might produce when they lack sufficient context or information.

To ensure that the data retrieved is always current, the vector database should be continuously updated with real-time information. This ongoing update process ensures that RAG pulls in the most recent and contextually relevant data available.

While LLMs excel at text generation, including translation and text summarization, they struggle to generate highly specific information based on real-time data. Retrieval-augmented generation (RAG) addresses this by retrieving external data to provide context for LLMs, enabling them to respond to queries more accurately and relevantly.

RAG glossary - chatbot

Consider an AI chatbot using an LLM without RAG. Without context, trustworthiness, or real-time data, LLMs cannot deliver meaningful value to users.

To improve information precision, RAG systems leverage semantic search and vector search. This allows context from retrieved data to enhance the relevance and accuracy of responses.

By matching input parameters with real-time data, RAG can address queries that need current information. For example, an airline chatbot using RAG can help a customer find alternative flights or seats by providing the most accurate, up-to-the-minute options. The combination of advanced search techniques with real-time data enables RAG to help LLMs generate contextually relevant responses tailored to the customer’s specific request.

Why RAG?

RAG glossary - why

LLM Challenges

LLMs are excellent foundational tools for building GenAI applications and have made AI more accessible. However, they come with challenges.

LLMs are stochastic by nature and generally trained on a large corpus of static data. As a result, they lack current domain-specific knowledge. When there is a knowledge gap, LLMs can provide hallucinated answers that are false or misleading despite sounding plausible. For businesses with GenAI applications, hallucinations can break customer trust, damage brand reputation, and even create legal issues.

LLMs can function as a ‘black box’ with unclear knowledge fidelity and provenance, which can lead to potential issues with data quality and trust.

RAG architecture addresses these issues by augmenting LLM responses with real-time data, without the high costs of re-training the model. Since LLMs are expensive to run, these costs are often passed on to customers based on the number of tokens processed. By querying a vector database, RAG can provide accurate answers by contextualizing prompts with the most relevant domain-specific information, reducing the number of calls to the LLM. This, in turn, lowers the number of tokens processed, thereby significantly reducing costs. LLMs have limited context windows of ‘attention‘ for each prompt, so this RAG pattern enables use cases that would otherwise be infeasible without filtering out only the most relevant information.

RAG architecture can also minimize privacy concerns related to sensitive information generated by LLMs. It allows sensitive data to be stored locally while still leveraging the speed of LLMs’ generative capabilities. RAG provides an opportunity to filter out private or sensitive data within a model’s knowledge library before sending the prompt to the LLM.

RAG glossary - LLM

Advantages of RAG over Pre-trained or Fine-tuned LLMs

RAG has distinct advantages over pre-trained or fine-tuned LLMs.

Pre-training involves training an LLM from scratch using a large dataset. While this allows for extensive customization, it requires significant resources and time investment.

Fine-tuning adapts pre-trained models to new tasks or domains with specialized datasets. Although more resource-efficient than pre-training, fine-tuning still demands considerable GPU resources and can be challenging. It may inadvertently cause the LLM to forget previously learned information or reduce its proficiency.

RAG, on the other hand, augments publicly available data from LLMs with domain-specific data from the enterprise. This allows for parsing and inferencing with context at prompt time. Additionally, post-processing in a RAG system verifies generated responses, minimizing the risk of inaccuracies or false information from the LLM.

RAG has emerged as a common pattern for GenAI, extending the power of LLMs to domain-specific datasets without the need for retraining models.

Benefits of RAG

Key benefits of RAG for generative AI include:

Access to real-time information

By using a real-time data streaming platform to keep the vector store up to date, RAG retrieves real-time data from sources such as operational databases or industrial data historians. This ensures that LLMs generate the most current and accurate responses.

Domain-specific, proprietary context

RAG incorporates information from proprietary and non-public data sources, allowing for tailored responses that align with specific industries, company policies, or user needs.

Cost-effectiveness

RAG reduces reliance on resource-intensive methods like pre-training or fine-tuning, making it a more cost-effective solution.

Reduced hallucinations

RAG mitigates the risk of hallucinations by providing the LLM with accurate, up-to-date information from reliable data sources. For example, if the vector store has up-to-date information provided by a Kafka topic, it can ensure that a chatbot accesses accurate and relevant inventory levels when responding to queries.