RETRIEVAL-AUGMENTED GENERATION CHATBOTS

Retrieval-Augmented Generation, or RAG for short, is an advanced AI technique that combines generative models (such as language models) with retrieval systems (information systems) to provide better, more contextually relevant answers. Where traditional AI models rely entirely on pre-trained knowledge, RAG can retrieve information from an up-to-date database or knowledge base and integrate it directly into the generated output. This makes it possible to answer questions more accurately, timely and reliably.

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How RAG works?

RAG works in two steps:

  1. Retrieval phase: When a question or request comes in, the AI ​​first searches an external database (e.g., documents, articles, or internal data). The most relevant information is retrieved and made available for the next phase.
  2. Generative stage: The retrieved information is then processed by a language model and used to generate a complete, coherent and contextual response.

This approach combines the power of retrieval with the flexibility of generation. It ensures that the AI ​​model can not only produce well-written answers, but also that these answers are directly supported by the most up-to-date and relevant data.

RAG Examples

  1. Customer Service: AI models equipped with RAG can quickly provide answers to customer questions by retrieving relevant information from FAQs, manuals, and product databases. This not only improves the speed, but also the accuracy and relevance of each answer.
  2. Medical Diagnostics: In medical AI applications, RAG can integrate current research results and medical knowledge, helping to provide patients and doctors with the most reliable and up-to-date information.
  3. Financial Analysis: Financial institutions can use RAG to generate market analysis, reporting and advice based on the latest market data, which is essential for investment decisions and risk management.
  4. Marketing Insights: RAGs can be very informative for marketing departments. Without technical and modeling knowledge, they can gain insights into their target group in a chat-related way.


Talk2DB

NEW! Talk2DB Model

Why write difficult queries and code when our model can do it for you? With Talk2DB you can retrieve data from your database using text and create insights that you may not have thought of yourself.

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At Trust AI, we believe that RAG is the future of trustworthy AI. By combining powerful language models with dynamic information retrieval techniques, we are able to provide solutions that not only generate answers, but also ensure accuracy, transparency and relevance.

Are you curious about how Retrieval-Augmented Generation can strengthen your organization? Contact Trust AI to discover how we can integrate RAG into your business processes for reliable and intelligent solutions.