Blog by Rahul Mittal

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…

Tokens, Embeddings and Vector Databases for Java Developers "Databases store data. Vector databases store meaning." Introduction In the previous article, we learned: What an LLM is. How transformers work. Tokens and context windows. Hallucinations. Training and inference. At this point, many developers ask: If LLMs are trained on old data, how can they answer questions about my company's documents? The…