RAG: AI that (finally) relies on your own data

Using AI to directly tap into your own data and generate ultra-precise answers: this is the promise of RAG (Retrieval-Augmented Generation). By combining research and generation, this technology increases the efficiency of AI (LLM ) and makes it possible to offer solutions perfectly adapted to business challenges. RAGs perfectly complement LLMs, offering them a new level of performance.

1. Definitions

   
   

1.1. LLM

A Large Language Model (LLM) is an artificial intelligence model designed to process and generate natural language. It relies on deep learning algorithms to analyze large amounts of text, identify linguistic structures, and produce answers. However, LLMs can inadvertently memorize and reproduce sensitive data used during training or previous questions, potentially disclosing confidential information.

1.2. RAG

Retrieval augmented generation (RAG) is an approach that combines a large language model with a search engine integrated with reliable data sources, such as internal documents, business databases, etc. This method allows AI to generate precise and contextualized responses, by accessing verified information.

2. Operating principle

3. Use cases

   
   

3.1. Intelligent document search

Navigate through large volumes of data (reports, standards, technical documentation, etc.) and obtain concise and contextualized answers, without having to go through tedious manual searching and reading. A true assistant for monitoring, analyzing or consulting business documents.

3.2. Customer Support

Connected to a knowledge base (FAQ, guides, product documentation), the RAG allows you to generate reliable responses to user requests in complete autonomy,

3.3. Technical and/or business support

The RAG provides enhanced technical and business support by making this knowledge available. This ability to provide relevant information in real time facilitates the resolution of complex problems and informs decision-making, even on complex or sector-specific topics.

4. RAG Challenges

   
   

4.1. Information structure

RAGs centralize internal resources (documents, business databases, processes) in a single repository. This organization reduces the dispersion of knowledge, facilitates information searches, and limits losses linked to data silos.

4.2. Gain in reliability and relevance

Unlike traditional models that generate text from general knowledge, RAG relies on up-to-date internal sources. This allows for the generation of consistent, traceable responses aligned with business challenges. Adapting to specific vocabulary and use cases further enhances the accuracy of the results, while reducing the risk of hallucinations.

4.3. Facilitated evolution

One of the major advantages of RAG is its ability to integrate new information without retraining. A document update is all it takes to instantly enrich the generated responses. This flexibility allows teams to quickly adapt to regulatory changes, internal developments, or feedback from the field.

5. RAG as a Service by thaink²

   
   

At thaink², we offer RAGs As A Service, a fully managed building block that allows you to easily create your own conversational agents from your business knowledge bases (databases, documents, etc.) and by choosing the LLM of your choice (ChatGTP, Mistral, LLama, etc.).

Discover it in the video below

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