Augmented RAG: How Data Machines Enable a Hallucination Free, Contextual Information Retrieval
In the last few posts, we discussed how to build AI solutions using a Data Machine for different business use cases. Amongst the use cases that help with productivity for businesses, information lookup and retrieval is one of the most prevalent requirements for businesses of all nature.
Businesses are constantly looking for ways to optimize their systems and find more efficient ways for their employees and customers to find information that can be helpful. Examples of such information can be Product Information, Product Recommendations, Technical support documentation, How-To guides, HR Policy information for employees, Content Reference for Marketing Teams etc.
With the advent of the improvements offered through Large Language Models (LLMs), information lookup and retrieval has become one of the most popular implementations of AI for businesses both in all sectors. The method of retrieving information from knowledge sources using the language capabilities of an AI model is commonly referred to as Retrieval Augmented Generation or RAG.
What is RAG?
Retrieval Augmented Generation is an advanced approach in natural language processing that combines the strengths of retrieval-based and generative models to improve the quality and accuracy of AI-generated content. In a RAG enabled AI model, when a user query is received, the system first retrieves relevant information or documents from a large database or knowledge base using a retrieval mechanism, such as a dense passage retriever.
This retrieved content is then used as a context or input for a generative AI model, to produce a more informed, contextually relevant, and accurate response. The integration of information retrieval with content generation allows the model to handle complex queries that require up-to-date or specialized knowledge that might not be fully captured in the model’s training data.
RAG models are particularly useful in scenarios where the information required is dynamic or too vast to be entirely embedded within a generative model’s parameters, such as answering domain-specific questions, customer support, or content generation based on specific datasets. By leveraging retrieval mechanisms, RAG ensures that the generated content is grounded in factual, relevant information, thereby reducing the likelihood of generating hallucinations or incorrect data, a common issue with purely generative models. This hybrid approach enhances the overall performance, making RAG models a powerful tool for applications that require both creative and accurate outputs.
What can still go wrong with RAG?
RAG was designed to minimize hallucinations inherent in the Generative AI models. So in its most basic implementation, RAG can eliminate hallucinations when the content being provided as reference is properly organized. However, businesses have information stored from various sources where the consumers of the information have different needs. A customer service representative will be looking for information from a different source than an HR manager, similarly a customer browsing a website would be looking for recommendations of products compared to a customer who is looking for some help with their order.
In scenarios like the above, a basic RAG implementation may help the AI model look for information within the available information provided to the model but that may not suffice in guiding the model to the appropriate data that is suitable for answering the question asked by the user. It would be a displeasing experience for the customer seeking help with their order, if they were recommended a product to purchase based on their query, or worse, if they were provided with the wrong information not related to their order.
What is Augmented RAG?
Augmented RAG is an implementation of the Data Machines platform by Qualetics. Augmented RAG is designed to address the complex needs of businesses where the information is never static, constantly changing and can have several branches of information for a variety of different needs.
Augmented RAG supports the following requirements necessary for a Secure, Hallucination Free experience –
Multi Tenancy- Support multiple users, departments, regions and customers of your business
Intent Detection- Detect the intent of the user before triggering information lookup for more accurate retrieval
User Based Information Retrieval- Provide each user their own search and retrieval mechanism from only the content they are permitted to access
Data Source Based Information Retrieval- Isolate retrieval of the information based on the Data Source of the information
Explicit Context Based Information Retrieval- Isolate retrieval of information based on the context supplied
Real-Time Monitoring- Observe every query and response being handled by the Data Machine and setup triggers on specific inputs or outputs
Feedback Capture to enable Self-Learning- Capture feedback from the user to help the A-RAG Enabled Data Machine to learn from the responses that were upvoted or marked helpful by the user
Continuous Automatic Fine Tuning- Gather information from Documents, Websites, API Endpoints, User Activity on Applications and Systems in addition to Transactions such as Orders, Form Submissions, Emails and Messages
Building an Augmented RAG enabled AI Solutions (Chatbot, Virtual Agent, Search) using a Data Machine
1. Click on the Data Machines navigation menu in the left navigation
2. Click on Add Data Machine
3. Drag an Operational Step from the toolbox
4. Select “Activity Models” in the category
5. Select any of the following Augmented RAG enabled models
-Q&A
-Fine-Tuned Text Generation
-Recommendations
-Deduplication
6. Drag and Drop the Final Step from the toolbox
7. Configure the options in the Final step based on your need
8. Test the Data Machine
9. Publish the Data Machine, if the Test is successful
10. Integrate the Data Machine using the available Rest API options or No Code options using Zapier
Sign Up for a Free Trial
To learn more about Data Machines and get started with building your own AI solutions, sign up for a Free trial and start building today.
Sign Up as a Partner
If you’re a Software Development firm looking to rapidly build AI solutions using a Data Machine for your customers, Data Machines offer you the best possibilities. Reach out to us here.