What do I need to know about RAG?
What do I need to know about RAG?
How RAG is rapidly changing the way companies and governments use generative AI
Voiceover narrated by Ken Herron
Image generated in Adobe Express
Introduction
People (including us!) talk about RAG because it's a revolution. With RAG, you feed AI with your existing information, such as documents and websites, to create a repository of contextual intelligence. This repository allows your chatbot to answer questions on that information without training the AI on every question.
Please write this down, "RAG is *not* a magic bullet." 1) Most users require some precisely controlled answers. For 100% accuracy and control, we use [narrow] conversational AI's defined questions, answers, intents, and flows. 2) RAG works with a limited set of formats. RAG can understand the content in a Dropbox folder of marketing files, but it can't pull data out of Excel spreadsheets. That said, RAG makes unstructured data easily accessible via conversational search.
What is RAG?
Retrieval-Augmented Generation (RAG) is the process of optimizing (you have likely heard the term “fine-tuning”) the output of a Large Language Model (LLM) by referencing an authoritative knowledge base (i.e, a PDF, URL, slide deck, user manual, product information, database, etc.) outside of the LLM’s training data before generating a response.
Why does everyone want to use RAG?
Companies and governments use RAG to enhance the accuracy, reliability, breadth, control, and safety of their generative Artificial Intelligence (AI) platforms (i.e., OpenAI ChatGPT, Google Gemini, Hugging Face HuggingChat, etc.).
How does RAG work?
The generative AI platform’s response is fine-tuned by using guardrails. In AI, a guardrail is a safeguard that prevents AI from causing harm to the company and its data. The guardrails also help to maintain the integrity and security of the generative AI platform.
Why do I care? How does using RAG benefit me?
RAG Increases Control
RAG allows the LLM to access and incorporate information from company and third-party knowledge sources beyond its training data. These knowledge sources include databases, articles, and other structured and unstructured data repositories and enable the company/brand or government to control the content of the responses without having to script every intent (i.e., question, answer, and variant).
RAG Increases Accuracy
By leveraging external knowledge with real-time/live data, RAG can generate responses that are more comprehensive, accurate, and contextually relevant than LLMs that rely solely on pre-trained, general-purpose, and legacy parameters.
RAG Increases Understanding
By allowing access to a wider range of company- and domain-specific information, RAG can better understand and interpret users’ queries and prompts. This results in responses that are better aligned with both the user and company/brand’s intent and provide more relevant information and assistance.
RAG Decreases Hallucinations
Incorporating external knowledge mitigates biases and inaccuracies/hallucinations in the LLM’s pre-trained data. By cross-referencing information with authoritative and company/brand sources, RAG produces responses that are more objective, reliable, and better aligned to the company or government’s messaging.
RAG Increases Expertise
RAG's ability to integrate external knowledge makes it adaptable to any domain or topic. By drawing on relevant information from selected company/brand/government sources, it can generate detailed responses tailored to specific contexts and industry verticals.
RAG Increases Flexibility
RAG allows companies and governments to have flexible information retrieval strategies, allowing the LLM to select the most relevant knowledge for each query. This flexibility enables a city chatbot, for example, to deftly handle diverse queries from residents and visitors alike.
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How does UIB use RAG?
In UIB’s patented, white-label Unified AI® chatbot builder platform, we use conversational AI (i.e., conventional Natural Language Processing (NLP) AI engines (i.e, IBM Watson, Google Dialogflow, Meta Wit.ai, etc.) to process the data from structured data sources and generative AI with RAG to simultaneously process from unstructured data sources, including integrations with live data.
Step 1: We collect and pre-process the data
Collect unstructured data from the different sources needed for the use case(s)
Pre-process the data to extract the text content and remove any irrelevant information (i.e., headers, footers, metadata, etc.).
Step 2: We construct the knowledge base
Use the pre-processed data (above) to build a comprehensive knowledge base for the RAG’s information repository.
Organize/Index the knowledge base to facilitate the efficient retrieval of information.
Step 3: We prepare the training data
Pair the structured knowledge base with prompts/queries representing the desired input-output pairs for the generative AI.
Train the RAG with these input-output pairs with supervised/reinforcement learning according to the use case’s needed tasks and objectives.
Step 4: We train
Use the knowledge base to augment RAG’s capabilities.
When presented with a prompt/query, the RAG retrieves relevant information from the knowledge base to improve its response generation.
RAG integrates the retrieved knowledge with its internal representations to produce the organization’s desired response.
Step 5: We fine-tune/optimize
Adjust the hyperparameters, optimize the retrieval strategies, and fine-tune the model architecture based on its validation performance.
Step 6: We evaluate and iterate
Evaluate the trained RAG against standard metrics and qualitative assessments to measure its effectiveness in generating the desired [relevant and coherent] responses.
Undergo further iterations as needed (i.e., based on the evaluation results), including retraining it on updated data and refining its retrieval and generation mechanisms.
Step 7: We deploy and integrate
Deploy the generative AI with RAG.
Integrate RAG into systems and applications (as needed), where it will operate autonomously or in conjunction with human users. The human users will further improve RAG's decision-making and information-retrieval capabilities.
Want to learn more about how UIB’s white-label Unified AI chatbot platform with conversational and generative AI can help your business to decrease costs, increase revenues, and delight users? Contact us today at info@uib.ai!
To learn more about RAG, check out AWS’ What is RAG? explainer.
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