NEURAL  NETWORKS

RAG (Retrieval-Augmented Generation) integrates advanced language models with document retrieval, providing accurate, context-aware answers drawn from private knowledge bases.

Solutions can be deployed using ChatGPT-4.0 via API or with open-source models hosted on dedicated infrastructure. Both approaches lead to production-ready systems designed to enhance decision-making, safety, and efficiency.

Typical applications include:

• Commercial chatbots that answer customer queries using company knowledge.
• Internal assistants that streamline workflows and support employees.
• Smart document manuals offering direct, context-aware answers.
• Compliance and policy guidance systems ensuring consistent decisions.
• Knowledge hubs that unify dispersed information for quick retrieval.

There are two main approaches to implementing RAG with large language models:

• API-based deployment (e.g. ChatGPT-4.0)
  Provides fast, reliable, and scalable solutions. Costs are predictable, and no specialized hardware is required.
• Local deployment (e.g. Mistral-7B)
  Runs directly on controlled infrastructure, maximizing privacy and independence from external providers. This option requires GPU-powered servers and involves higher operational costs.

The right choice depends on priorities such as performance, cost structure, and data security. Both approaches lead to robust, production-ready applications capable of adapting to the specific needs of each organization.