Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Generative AI capability

Overview of the Generative AI capability, encompassing the underlying technology, its application in ATRIA and the benefits derived from its use

Introduction to Generative AI

What is Generative AI?

Generative Artificial Intelligence is a subset of Machine Learning that focuses on the creation of new content, such as text, images, or music, based on patterns learned from large volumes of data.

This technology has advanced significantly in recent years, fueled by the development of Deep Learning models that can understand and replicate complex data structures.

Figure 10. Generative AI technology

Below are the main steps in how Generative AI works:

Training: The model is fed with extensive datasets containing examples of the target content, allowing the system to identify patterns, structures and relationships among different elements.
Instruction input: The user provides an instruction or “prompt,” which can be a question, a topic, or any indication of what is expected from the model output.
Content generation: Based on the information obtained during training, the model applies complex algorithms to generate a relevant and coherent response or new content aligned with the user’s request.
Response delivery: The model presents the generated output to the user quickly and efficiently. It can be a text, an image, or any other type of content.

Within Generative AI, the Large Language Models (LLMs) are advanced AI models designed to understand and generate human-like text, typically trained on vast amounts of text data, enabling them to predict and produce coherent and appropriate text. They are the ones integrated into ATRIA.

Benefits and limitations

The main benefits from the use of Generative AI are summarized below:

Creativity and Originality: Generative AI generates new and original content that can inspire creators.
Efficiency: Generative AI automates content generation tasks, allowing humans to focus on more complex activities.
Personalization: Generative AI generated content is tailored to the specific needs of users.
Access to information: Generative AI provides quick answers to complex questions thanks to its extensive access to data.

Despite these advantages, Generative AI has certain limitations that led ATRIA to integrate other complementary technologies:

Hallucinations: Generative AI can generate inaccurate responses that seem plausible, leading to misinformation.
Temporal Limitations: Generative AI models are limited to the information available at the time of their last training, meaning they cannot access real-time or recent data updates.

Application of Generative AI in ATRIA

Generative AI is a key ATRIA capability provided by a predefined chain designed with the LLM/LMM Experiences Builder.

ATRIA enables the generation of experiences (use cases) to resolve users' requests expressed in natural language by supporting simple calls to AI models.
This is done through an easy integration of advanced Generative AI technologies while guaranteeing security and privacy in interactions.

Figure 11. Generative AI in ATRIA

Example case

Imagine that our platform, ATRIA, operates like a restaurant with different chefs, each specialized in a unique approach to meeting customers' needs.

A traditional generative model can be compared to Chef Manuel, a chef who spent several years mastering in traditional Spanish cuisine.

Manuel’s expertise encompasses a wide range of recipes and cooking techniques, but some of his knowledge may be outdated since he hasn’t pursued further training in recent years.

When a customer requests for a nutritious and hearty meal, Manuel relies solely on his internal knowledge to prepare a classic dish: lentils with vegetables. He does not need to search for additional information because his prior expertise is sufficient to offer a consistent and reliable answer.

A traditional generative model operates like Manuel, generating responses based solely on the implicit knowledge learned during the model's training, without consulting external sources.

Interaction with ATRIA Generative AI

This service is accessible via API, enabling its consumption both from Aura Platform and any external application.

Current available models

The AI-driven models currently integrated into ATRIA are included here.

Functional overview

The use of this capability encompasses different stages:

When a user sends a request to ATRIA, it is sent to an auto-generative content generator, the one that best aligns with the use case considering different factors such as latencies, costs, etc.
Additionally, specific instructions upon which the model must base its response are also included. These instructions can be configured to meet specific channel-level business and experience requirements but, at the same time, to ensure that the provided responses retain the nuances of tone and personality that characterize Aura.
In addition, ATRIA provides a layer of security to avoid prompt injection, that is, to prevent misuse by third-party services that can create malicious prompts as inputs and cause the model to act in unintended ways.
For example, it can prevent a user from modifying the instructions on how the system should behave or the invalidation of instructions from a predefined block of the prompt (Aura personality), if contradictory instructions are given.
The Generative AI model recognizes the request and generates the most appropriate response for it. This response is sent back to the user.

Benefits from the use of Generative AI in ATRIA

There are clear benefits derived from the integration of Generative AI in ATRIA:

Benefits for constructors

It streamlines the use cases development process, since there is no need to generate specific responses or undergo specific trainings.
Other types of experiences, not directly related to Aura, can be generated. For example: data analysis tasks, development of new products, etc.

Benefits for end-users

Our customers’ satisfaction will increase, as Aura can offer enhanced understanding capabilities.
Aura can incorporate new areas of interest for users in a more agile manner and explore new types of users for whom to develop services based on natural language recognition technologies.
ATRIA interactions guarantee security and privacy for our users.

Generative feedback functionality

When testing how Generative AI/RAG capabilities work with the ATRIA web interface aura-manager, it is possible to use the feedback functionality to estimate the user’s satisfaction regarding the quality and appropriateness of the generated answer to her request. This can be done easily by clicking the thumbs-up or thumbs-down icons.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

RAG capability

Overview of the RAG capability, the benefits derived from its use and the current predefined RAG chain in ATRIA

Introduction to RAG technology

RAG (Retrieval Augmented Generation) is a technique for augmenting LLM knowledge with additional data. It provides a way to optimize the output of an LLM with targeted and updated information without retraining it; thus, providing more appropriate answers based on specific and latest data.

The process includes three differentiated parts:

Retrieval: it searches and extracts relevant information from a KB database using information retrieval techniques, such vector representations (embeddings) to find text blocks that contain the appropriate information to resolve the input request.
Augmented: the RAG model augments the user input (or prompts) by adding the relevant retrieved data. This step uses prompt engineering techniques to communicate effectively with the LLM.
Generation: the enriched prompt is sent to an LLM, that generates the most accurate response for the user.

Figure 12. RAG technology

Application of RAG in ATRIA

As explained before, the LLM/LMM Experiences Builder enables the generation of LLM chains that integrate different AI technologies.

Within this capability, complex flows based on the RAG technology can be integrated.

Example case

Imagine that our platform, ATRIA, operates like a restaurant with different chefs, each specialized in a unique approach to meeting customers' needs.

A RAG model can be compared to Chef Sara, a chef who combines her traditional culinary experience with the real-time consultation of resources to enhance her recipes with the latest culinary trends worldwide, as she likes to be continuously up-to-date.

When a customer requests a nutritious and hearty meal, Sara goes beyond her own knowledge, based on already learnt techniques and recipes. Instead, she consults innovative cuisine resources: Indian cookbooks and her recent notes on advanced molecular cooking techniques. These external sources allow her to innovate and propose a unique dish: a curry foam, light and airy, with an intense spice flavor and a touch of coconut milk.

In technical terms, the RAG approach combines:
a. Generation based on prior knowledge (the internal model): equivalent to Sara's knowledge of cooking.
b. Real-time retrieval of external information: consulting cookbooks and notes represents how a RAG system looks up information in databases or dynamic sources during the response process.

This integration allows the model to provide more contextualized responses, tailored to specific needs, especially when the stored knowledge is limited or insufficient.

Currently, ATRIA incorporates the following RAG chains:

General RAG: Complex AI-driven flow for resolving generic questions experiences based on FAQs
SQL RAG: RAG-based pipeline for resolving SQL queries

In upcoming versions, constructors will be able to design their own LLMs chains based on RAG.

Benefits from the use of RAG technologies

Updated and targeted information: RAG allows developers to provide the latest data to the generative models, targeted to the specific use case.
Cost-effective implementation: Data in the knowledge repository can be continually updated without incurring significant costs.
Enhanced user trust: The data sources contributing to the RAG’s vector database are identifiable. This transparency allows for the correction or removal of any inaccuracies present in RAG and clearly improves users’ confidence.
Improved developers control: With RAG, developers can test and improve their applications more efficiently, control and change the LLM’s information sources to adapt to changing requirements, restrict sensitive information retrieval to different authorization levels and ensure the LLM generates appropriate responses.

Aura –

Docs:

Generative AI capability

Introduction to Generative AI

What is Generative AI?

Benefits and limitations

Application of Generative AI in ATRIA

Interaction with ATRIA Generative AI

Current available models

Functional overview

Benefits from the use of Generative AI in ATRIA

Generative feedback functionality

Docs:

RAG capability

Introduction to RAG technology

Application of RAG in ATRIA

Benefits from the use of RAG technologies