Structure of the course: Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents

Description

Structure of the Udemy course: Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents https://www.udemy.com/course/complete-genai-with-java-spring-ai-llms-rag-ai-agents/?couponCode=7AB0FE0C024AEFADA503 The concepts that you will be learning and implementing are: - Building end-to-end GenAI systems in Java and Spring AI with advanced Spring AI concepts - Designing RAG pipelines with vector databases, embeddings, similarity search and semantic search using advanced ingestion and retrieval strategies such as query transformer, query expander, pre/post processors, re-ranker, metadata filtering and dynamic resource updates - Creating AI agents with tool/function calling using autonomous and chained workflow agentic systems - Implementing chat memory and long-term context with in-memory, jdbc and vector store backends using Spring AI advisors - Applying prompt engineering best practices and defend against prompt hacking techniques including prompt injection, jailbreaking and prompt leaking attacks - Using MCP (Model Context Protocol) for distributed AI systems, creating MCP Server and MCP client using Spring AI - Adding Observability (logs, traces, metrics) to AI applications - Learning Gen AI and LLM Fundamentals with Tokenizers, Embeddings, Positional encoding, Transformer architecture, Token prediction and Softmax formula - Mapping the Gen AI and LLM Fundamentals into practical solutions - Understanding LLM limitations and possible mitigations