Chapter 1- Build Production-Ready AI Agents with Spring AI, and Java
Introduction Artificial Intelligence has evolved rapidly over the past few years. Initially, AI applications were limited to answering questions, summarizing text, translating languages, or generating content. These applications are commonly referred to…
Retrieval-Augmented Generation (RAG): Architecture, Pipeline, and Enterprise Implementation
Introduction Large Language Models (LLMs) such as GPT, Claude, Gemini, and Llama have transformed how organizations build AI-powered applications. These models excel at understanding language, reasoning, and generating human-like responses. However, they…
Building Enterprise AI Applications with RAG and LangChain
Large Language Models (LLMs) such as GPT, Claude, and Gemini are excellent at understanding language, reasoning, and generating human-like responses. However, they have one major limitation—they do not know your organization’s private knowledge…
From ChatGPT to AI Agents to MCP: Understanding the Evolution of Enterprise AI
Introduction Over the past couple of years, we’ve all become familiar with AI tools like ChatGPT, Claude, Gemini, and Microsoft Copilot. They can answer questions, summarize documents, write emails, generate presentations, and even help us write…
How to design a Recommendation System- use ML/Machine Learning Algorithms
1. Requirements Gathering Functional Requirements We need a system that: Non-Functional Requirements Requirement Target Relevance High CTR/Conversion Latency <100ms Availability 99.9% Freshness Minutes or seconds Scalability Millions of users 2.…