Large Language Models (LLMs) are a category of artificial intelligence models designed to understand, generate, and work with natural language on a large scale. These models are trained on huge datasets of text collected from a wide range of sources to learn language structures, context, semantics, grammar, and the nuances of human language.

This makes them particularly effective for natural language processing (NLP) tasks.

LLMs can understand text in an advanced way, enabling them to answer questions, summarize documents, translate languages, and much more.

They are capable of producing coherent and contextually relevant text, ranging from responses to specific questions to content creation.

SISTAAR AI coordinates the interaction between all artificial intelligence components:

OpenAI API REST https + Auth Token: An application programming interface that allows communication with OpenAI services through the secure HTTPS protocol, using an authentication token to ensure security and authorized access.

Prompt and Context: The mechanism through which users provide a textual input (prompt) to the AI, along with an optional context, to guide the generation of specific responses or content in line with the user's needs.

Data Integration: The process of combining data from different sources (such as ERP, CRM, e-commerce, etc.) into a single system or application, to allow for unified analysis and access through artificial intelligence solutions.

Memory: The system's capacity to store information or previous conversation contexts, allowing the AI to provide more coherent and personalized responses over time.

RAG and Embeddings: Retriever-Generator (RAG) combines information retrieval with text generation to answer questions or perform tasks based on a vast corpus of text. Embeddings are vector representations of text that facilitate this search and understanding of natural language.

Vector Database Search: A technique that uses databases optimized for vectorial search to quickly find the most relevant information in large datasets, based on semantic similarity rather than exact match.

Chunking: The method of dividing long or complex texts into smaller parts (chunks) to improve processing, understanding, and response generation by artificial intelligence.

LLM Integration: Integration of Large Language Models (such as GPT) into software applications, to leverage their advanced capabilities in understanding and generating natural language for various tasks, such as answering questions, content creation, and more.

UI Integration: Implementation of user interfaces that facilitate interaction between users and artificial intelligence-based solutions, such as chatbots or virtual assistants, making the experience more intuitive and accessible.