LLM (Large Language Model) models 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 enormous text datasets collected from various sources to learn linguistic structures, context, semantics, grammar, and nuances of human language.

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

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

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

SISTAAR AI coordinates the interaction between all artificial intelligence components:

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

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

Data Integration: Process of combining data from different sources (such as ERP, CRM, e-commerce, etc.) into a single system or application, enabling unified analysis and access through AI solutions.

Memory: System's ability 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 text corpus. Embeddings are vector representations of text that facilitate this search and understanding of natural language.

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

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

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

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