Integrating RAG and Composite AI to optimize Conversations with Users | Almawave
x icon pop up DISCOVER MORE

Search the site

Didn't find what you were looking for?

AdobeStock_639917274

Artificial Intelligence

22 April 2024

Integrating RAG and Composite AI to Optimize Conversations with Users

Harnessing the potential of generative AI simplifies complex processes and transforms the way they interact with information systems leading to more immediate, intuitive user experiences and innovative applications that offer substantial support while enhancing efficiency and productivity.

Generative AI models can be used for many purposes and in different contexts. Their main ability is to generate content from natural language instructions (prompts). 

Some of the most popular generative AI systems are based on Large Language Models (LLMs), which are language models trained on massive amounts of linguistic data with deep capabilities to understand, generate, and transform natural language content.

Very often, users use LLMs to obtain information of various kinds or to carry out simple linguistic tasks in the context of ordinary daily operations, such as summaries, requests for suggestions or translations. 

The Limits of Generative Artificial Intelligence

Using LLMs to gain insights (especially in a business application context) can have some limitations.

The information provided by LLMs is not always reliable or verified. These models are unable to provide up-to-date information in real-time, and are sometimes unable to understand when they don’t have enough information to answer a question. Their answers can sometimes be incoherent or present contradictions, and they do not indicate the sources of the information. Furthermore, LLMs are unable to integrate information from sources other than the training data on which they were developed.

Refining AI with Retrieval Augmented Generation

One way to overcome these limitations is through Retrieval Augmented Generation (RAG): a technique that combines the generative capabilities of LLMs with those of retrieving information from an external source of knowledge. 

By using RAG, you can provide correct and reliable answers, based on up-to-date data that is specific to the application context. 

RAG operates through two distinct phases: recovery and generation: 

  • Retrieval phase:  Retrieval algorithms search for the information requested by the user within predefined knowledge bases. 
  • Generation phase: Once this information has been obtained, the retrieved data is transformed into a linguistically appropriate response to the user’s request.

Therefore, it is clear that one of the most significant areas of application for RAG is conversational AI.

In fact, RAG can be used to enhance the functionality of chatbots and virtual assistants by making them able to provide more accurate and complete answers to users’ questions. 

Almawave and the innovative "composite" approach

At Almawave we believe in a “composite” AI approach, one that addresses various business challenges by integrating multiple analytical techniques, such as Machine Learning, Deep Learning (with pre-trained models), and Knowledge Graph technologies to boost the performance and potential of the various technologies. 

This approach is called “Generative Composite AI”.

Almawave’s Generative Composite AI solutions   include innovative products and tools which allow our clients to access information in a simpler and more immediate way, allowing companies to make their processes faster and more efficient

Our approach prioritizes security and reliability, allowing for a better user experience and transparency, because it is enhanced by RAG and the ability to give AI access to multiple additional data sources that make its answers more complete and error-free.

Through the combination of the various techniques and technological components,we can overcome the typical constraints of LLMs.This allows us to use  both RAG as well as other strategies to offer our clients solutions that are able to:

  • Leverage generative AI to query structured data through the use of natural language queries to provide answers, navigate graphs, obtain synthetic indicators, and generate reports. Access to structured data can also be mediated by a layer of semantic-ontological reasoning that makes it possible to increase the ability to understand and access corporate information, regardless of the physical structure of the data storage that manages it (Trusted Natural Query).
  • Dynamically manage, with “zero-shot” algorithms, a knowledge base of specific answers, to be provided in response to all those requests that require a “certified” response. We are skilled at intercepting and managing requests that require a guided response path. This makes it possible to invoke an external service, which must then be identified through a precise classification of the user’s request and possible collection of the information necessary for the invocation of a specific API.
  • Quickly identify situations where human support is needed, thanks to a fully integrated, multi-channel digital control unit that provides a “seamless” support experience.

Adopting Generative Composite AI in conversational AI has completely revolutionized traditional chatbots, including widely recognized ones like ChatGPT.

In addition to an improvement in the ability to understand and generate language, conversational assistants that rely on this approach are also able to provide users with reliable and context-appropriate answers, thus ensuring a satisfactory user experience and a significant reduction in time and costs.