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What is RAG? — Retrieval-Augmented Generation Explained

Author’s note: RAG is covered in my latest course, “Generative AI: ChatGPT & OpenAI LLMs in Python”. Click here to get it:

Discover the Power of Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) revolutionizes the potential of large language models (LLMs) by integrating external authoritative knowledge sources into the generation process. While LLMs excel at tasks like answering questions and translating languages, RAG takes their capabilities further by seamlessly incorporating domain-specific or internal organizational knowledge. This enhancement ensures that generated content remains pertinent, precise, and valuable across diverse contexts, all without the need for extensive retraining (which can be costly and time-consuming).

Why is Retrieval-Augmented Generation crucial?

Large language models (LLMs) play a pivotal role in driving artificial intelligence (AI) technologies like chatbots and natural language processing (NLP) applications. These systems aim to provide users with accurate responses drawn from authoritative knowledge bases across various contexts. However, LLMs come with inherent challenges. They may output incorrect or outdated information when confronted with queries beyond their training scope, leading to potential loss of user trust.

Key challenges of LLMs include:

  1. Presenting inaccurate information when unable to provide a correct response.
  2. Delivering stale or generic responses instead of specific, up-to-date information.
  3. Drawing from non-authoritative sources to formulate responses.
  4. Generating inaccurate responses due to terminology discrepancies among training sources.

To illustrate, imagine an eager but ill-informed new employee who confidently responds to every query, regardless of accuracy. He is eager to please, but not as knowledgeable as his more experienced coworkers. His goal is to provide a response you’ll like, instead of a response that’s correct. Such behavior erodes user confidence and undermines the effectiveness of chatbot interactions.

Retrieval-Augmented Generation (RAG) emerges as a solution to these challenges. By leveraging predefined authoritative knowledge sources, RAG enhances LLM responses, granting organizations greater control over generated content while providing users with insights into response generation processes.

What are the advantages of Retrieval-Augmented Generation?

Implementing RAG technology yields several benefits for organizations investing in Generative AI (GenAI) solutions.

Cost-effectiveness: Traditional chatbot development typically starts with foundation models (FMs), which are API-accessible LLMs trained on diverse, unlabeled data. Retraining FMs for organization-specific or domain-specific information incurs significant computational and financial expenses. RAG offers a more cost-effective alternative by seamlessly integrating new data into the LLM, making generative AI technology more accessible and practical.

Up-to-date information: Even if the original training data for an LLM aligns with organizational needs, maintaining relevance over time presents challenges. RAG empowers developers to infuse generative models with the latest research findings, statistical updates, or real-time news. By linking LLMs directly to dynamic sources like social media feeds or live news sites, RAG ensures users receive the most current information available.

Enhanced user confidence: RAG enables LLMs to furnish accurate information with clear source attribution. Generated content can include citations or references, allowing users to verify information independently if needed. This transparency fosters trust and confidence in the generative AI solution, enhancing the user experience.

Increased developer control: RAG empowers developers to iterate and refine chat applications more efficiently. They can tailor the LLM’s information sources to adapt to evolving requirements or diverse use cases. Moreover, developers can implement access controls to restrict retrieval of sensitive information based on authorization levels, ensuring the LLM generates appropriate responses. Additionally, they can troubleshoot and rectify instances where the LLM references incorrect information sources for specific queries. This enhanced control enables organizations to deploy generative AI technology with greater confidence across various applications.

How does Retrieval-Augmented Generation (RAG) work?

In the absence of RAG, the LLM formulates responses based solely on its training data or existing knowledge. However, with RAG, an additional information retrieval component comes into play. This component leverages user input to retrieve pertinent information from external data sources before presenting it to the LLM for response generation. The subsequent sections outline the process in detail.

External Data Creation: External data refers to information beyond the scope of the LLM’s original training dataset. It can be sourced from diverse outlets such as APIs, databases, or document repositories, and may exist in various formats, including files, database records, or lengthy textual documents. Through techniques like embedding language models, this data is transformed into numerical representations and stored in a vector database, forming a knowledge library accessible to generative AI models.

Retrieval of Relevant Information: The next step involves conducting a relevancy search. The user’s query is converted into a vector representation and compared against entries in the vector databases. For instance, in the scenario of a smart chatbot handling human resource inquiries, a query like “How much annual leave do I have?” would trigger retrieval of relevant documents such as annual leave policies and the employee’s historical leave records. The relevancy assessment relies on mathematical vector calculations and representations to identify highly pertinent documents.

Augmentation of LLM Prompt: Subsequently, the RAG model enriches the user input (or prompts) by integrating the retrieved data within context. This step employs prompt engineering techniques to effectively communicate with the LLM. By augmenting the prompt, the large language models can furnish accurate responses to user queries.

Update of External Data: To address potential staleness of external data, proactive measures are taken to ensure information currency. This involves asynchronous updates to documents and corresponding updates to their embedding representations. Updates can be performed through automated real-time processes or periodic batch processing, aligning with established data management practices in analytics.

The following diagram illustrates the flow of data when using RAG with LLMs.

What’s the Difference? Semantic Search vs. RAG

Semantic search complements RAG by enhancing the results for organizations seeking to integrate extensive external knowledge sources into their LLM applications. In modern business, vast repositories of information such as manuals, FAQs, research reports, customer service guides, and human resource documents are scattered across multiple systems. Retrieving context at scale poses a significant challenge, which can consequently diminish the quality of generative output.

Semantic search technologies excel in scanning large databases of disparate information and retrieving data with higher accuracy. For instance, they can accurately address queries like “How much was spent on machinery repairs last year?” by efficiently mapping the question to relevant documents and returning specific text instead of mere search results. Developers can leverage these precise answers to provide richer context to the LLM.

In contrast, conventional or keyword-based search solutions within RAG often yield limited results for knowledge-intensive tasks. Developers also grapple with complexities such as word embeddings and document chunking while manually preparing their data. Semantic search technologies alleviate these burdens by automating knowledge base preparation, sparing developers from manual labor.

Where to get the code for RAG with OpenAI and ChatGPT / GPT-4

The code to implement RAG (Retrieval-Augmented Generation) in Python using ChatGPT or GPT-4 as LLMs with the OpenAI API can be found in my latest course, “Generative AI: ChatGPT & OpenAI LLMs in Python”.

Click here to get it: