Ai

Oracle among other companies announced recently that 50+ role-based AI agents within the Oracle Fusion Cloud Applications Suite will help successfully execute frequent, repetitive tasks. Other companies are doing the same. In this article I will discuss what AI agents are, what are some of the use cases and link some tools/frameworks that can help you design and build agents.

RAG is one of the most common use cases that has been implemented in the past couple of years. Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of LLMs by combining them with external knowledge sources. It involves retrieving relevant information from a knowledge base, incorporating it into the LLM's context, and then generating a response that leverages both the LLM's internal knowledge and the retrieved information. Building RAG applications requires integrating various components like vector databases and search algorithms, which can be quite involved. In this blog we'll briefly talk about RAG basics and leveraging OpenAI's assistants to build simple RAG applications.

This blog post will take you through the process of building a recommendation system and the concept of embeddings, vector databases and various use cases. These concepts are not only limited to recommendation systems but are widely used in various domains such as image recognition, natural language processing, semantic search, and anomaly detection. The ability to represent complex, high-dimensional data in a dense, lower-dimensional space is a fundamental technique in machine learning.

AI adoption is accelerating across industries, transforming how businesses operate and innovate. As companies embrace AI, it is crucial to understand the security and privacy implications. This article will explore security considerations when building custom AI solutions and integrating AI into business operations.