Tag: ai

Building an Enterprise Architecture Copilot: The Complete Capstone Project "The best way to learn AI engineering is not by building chatbots. It is by building systems that solve real enterprise problems." Introduction Over the previous eleven articles, we have covered: LLM Fundamentals Embeddings Vector Databases RAG Spring AI Prompt Engineering Agents Multi-Agent Systems LangGraph MCP Production Deployment on AWS Now…

Building Production AI Systems on AWS: From Proof of Concept to Enterprise Deployment "An AI demo impresses people. A production AI platform delivers business value." Introduction Over the last ten articles, we have learned: LLMs Embeddings Vector databases RAG Prompt engineering Spring AI Agents Multi-agent systems LangGraph MCP At this point, you can build: ✅ Chatbots ✅ RAG systems ✅…

Model Context Protocol (MCP) and Tool Integration: Connecting AI to Enterprise Systems "An LLM without tools is knowledgeable. An LLM with tools becomes useful." Introduction In the previous articles, we learned: Part 1 — LLM Fundamentals Part 2 — Embeddings and Vector Databases Part 3 — RAG Part 4 — Building RAG Applications Part 5 — Prompt Engineering Part 6…

Multi-Agent Systems and Agentic AI Architectures "A single agent can solve a task. A team of agents can solve a business problem." Introduction In Part 7, we learned that an AI agent can: Reason Plan Use tools Maintain memory Execute tasks But enterprise systems rarely rely on a single component. We don't build one giant Java class. We build: Controllers…

Building AI Agents: Reasoning, Planning, Tools and Autonomous Workflows "A chatbot answers questions. An agent solves problems." Introduction So far in this series, we have learned: Part 1 — LLM Fundamentals Part 2 — Embeddings and Vector Databases Part 3 — RAG Architecture Part 4 — Building RAG Applications Part 5 — Prompt Engineering Part 6 — Spring AI Deep…

Spring AI Deep Dive: ChatClient, Advisors, Memory, Tool Calling and Structured Output "Spring Boot simplified enterprise development. Spring AI aims to simplify enterprise AI development." Introduction In our journey so far, we have learned: Part 1: LLM Fundamentals Part 2: Embeddings and Vector Databases Part 3: RAG Architecture Part 4: Building a RAG Application Part 5: Prompt Engineering Now we…

Prompt Engineering for Enterprise AI: The New API Design "In traditional software, APIs define behavior. In AI systems, prompts define behavior." Introduction In our previous articles, we learned: Part 1: LLM Fundamentals Part 2: Embeddings and Vector Databases Part 3: RAG Architecture Part 4: Building RAG Applications with Spring AI At this point, many developers assume: "The AI model is…

Building Your First RAG Application Using Spring AI "Theory teaches concepts. Projects teach engineering." Introduction In the previous articles we learned: Part 1 LLMs Tokens Transformers Part 2 Embeddings Vector databases Semantic search Part 3 RAG architecture Retrieval pipelines Chunking Similarity search Now it is time to build our first application. Our goal: Upload documents and ask questions about them.…

Retrieval Augmented Generation (RAG): Building AI Systems That Know Your Data "The most valuable enterprise AI systems are not trained on the internet. They are built on your organization's knowledge." Introduction In the previous articles we learned: Part 1 LLMs Tokens Context windows Transformers Hallucinations Part 2 Embeddings Vector databases Similarity search Chunking Semantic retrieval Now we combine everything. This…