What is RAG ?

Introduction

In the ever-evolving world of artificial intelligence, retrieval augmented generation (RAG) presents itself as a promising innovation, capable of propelling the capabilities of large language models (LLMs) to new heights.

Imagine an AI system that, not content with drawing solely on its own knowledge, can explore the vast universe of external information to enrich its answers, making them more accurate, relevant and informative. This is precisely what AGR enables.

Definition of Recovery Augmentation Generation (RAG)

Recovery Augmentation Generation (RAG) is a technique that complements text generation with information from private or proprietary data sources. It combines a retrieval model, designed to search large datasets or databases, with a generation model such as a large language model (LLM), which extracts information and generates a readable text response.

Augmented retrieval generation can improve the relevance of the search experience, by adding context from additional data sources and supplementing an original LLM database through training.

This improves the results of the large language model, without the need to retrain the model. Other sources of information can range from new information on the Internet for which the LLM has not been trained, to proprietary business context, to confidential internal documents belonging to companies.

GAN is valuable for various tasks, such as question answering and content generation, as it enables the generative AI system to use external information sources to produce more accurate and context-sensitive answers. It implements search retrieval methods, typically semantic search or hybrid search, to meet the user’s intent and

So, what is information retrieval ?

Information retrieval refers to the process of searching for and extracting relevant information from a knowledge source or data set. It is similar to using a search engine to look for information on the Internet. You enter a search, and the system retrieves and shows you the documents or web pages most likely to contain the information you’re looking for.

Information retrieval involves techniques for efficiently indexing and searching large data sets, making it easier to access the specific information you need from a large pool of available data. In addition to Web search engines, IR systems are often used in digital libraries, document management systems and various information access applications.

How does RAG work? :

  • Retrieving relevant information : The first step is to identify and extract relevant information from an external knowledge base. This is usually done using a search query formulated from the initial prompt given to the LLM.
  • LLM prompt enrichment : The information retrieved is then integrated into the prompt provided to the LLM. This provides the model with a broader and richer context in which to generate its response.
  • Text generation : The LLM then uses the enriched prompt to generate coherent, informative text, taking into account both information from the external knowledge base and its own internal knowledge.
  • Updating the external knowledge base : As the RAG system is used, LLM performance can be improved by updating the external knowledge base with new interactions and results generated.

What are the advantages of RAG ?

There are several advantages to using RAG :

  • Improved accuracy and relevance of answers : By drawing on external knowledge, LLMs can generate answers that are more factually correct and better adapted to the context of the query.
  • Increased creativity and originality : Access to external information can stimulate LLMs’ creativity, leading them to generate more original and unexpected responses.
  • Adaptation to specific fields : RAG can be particularly useful for adapting LLMs to specific fields by integrating specialized knowledge bases.
  • Reduced need for training data : The use of RAG can reduce the amount of training data required to achieve satisfactory LLM performance.

What are the applications of RAG ?

RAG has the potential to be used in a wide range of applications, including :

  • Chatbots and virtual assistants : RAG can enable chatbots and virtual assistants to provide more precise and informative answers to user queries.
  • Content authoring : RAG can be used to automatically generate blog posts, reports, marketing documents and other forms of textual content.
  • Text summary : RAG can help you summarize long documents by identifying key information and rephrasing it concisely.
  • Machine translation : RAG can improve the quality of machine translation by integrating external linguistic and cultural knowledge.

RAG with ALLONIA :

Our SECURE ALLONIA CHAT uses the RAG method, making answers more relevant and precise.

Together with internal documents from reliable, verified sources, RAG enables the language model to cross-reference its data and draw on concrete facts to generate its answers. This reduces the risk of drifting towards inventions or misinterpretations. Less risk of hallucination!

Our SECURE CHAT is built with the ability to evaluate responses: the thumbs-up feature enables users to provide direct feedback on the quality of responses generated by the chatbot. By indicating whether a response is useful, relevant and informative, users help the system to learn and improve over time.

The exact passage is given as soon as the chatbot responds to your query. This allows you to read only the passage you want, rather than the whole document. Optimize your time with this ingenious tool !

You can also get summaries, which once again frees up your time for more important tasks !

1/ why use the ALLONIA chatbot : For ALL these reasons :

✅Secure and efficient
✅Adapts to all doc corpora.
✅We use the latest and greatest LLMs (Mistral, Open AI, Meta AI, Lighton, Anthropic).
✅No output
✅100% sovereignty and SecNumcloud & HDS hosting (Numspot, Outscale, Scaleway)

2/ For Who ?
Our chat has been designed for businesses and public authorities, and more specifically for your employees, customers or visitors.

3/ How does it work ?
As specified, no data leakage : The chat is trained only on the desired document corpus.
➡️Natural language responses
➡️Mention of sources and passages
➡️Dynamic corpus management

🎯responds to 3 major challenges :

➡️ Save your employees time by facilitating the use of internal documents.
➡️ Get fast, reliable and appropriate answers.
➡️ Guarantee the confidentiality of interactions.

use our secure chatbot free of charge for 2 weeks (then, from €30/month per user). So shall we take you on board ? Your employees will thank us 😉

Conclusion

RAG is an evolving field of research with great potential for enhancing the capabilities of LLMs and making them more useful in a wide range of applications.

In conclusion, augmented retrieval generation is a promising approach to overcoming some of the limitations of LLMs and paving the way for innovative new applications in AI.