| 1 -Course Introduction.mp4 | 177.4 MB | ||
| 1 -LLMs Don’t Remember You – The Stateless Nature of AI Models.mp4 | 42.5 MB | ||
| 1 -Thank You & Congratulations.mp4 | 19.5 MB | ||
| 1 -Tracing the Roots - How We Reached Generative AI.mp4 | 32.6 MB | ||
| 1 -Understanding Message Roles in LLMs - Theory.mp4 | 58.6 MB | ||
| 10 -How Embedding Vectors calculated.mp4 | 92 MB | ||
| 10 -Understanding ChatOptions in Spring AI.mp4 | 89.1 MB | ||
| 10 -Working with Multiple Chat Models in Spring AI.mp4 | 91.1 MB | ||
| 11 -Configuring ChatOptions in Spring AI.mp4 | 59.2 MB | ||
| 11 -What are Static Embeddings.mp4 | 44.5 MB | ||
| 12 -Positional Embeddings - How AI Understands Word Order.mp4 | 32.2 MB | ||
| 12 -Spring AI ChatClient – Response Types Explained.mp4 | 50.7 MB | ||
| 13 -Streaming AI Responses in Real-Time using stream() Method.mp4 | 33.2 MB | ||
| 13 -The Magic of Attention - How AI Understands Context.mp4 | 76.6 MB | ||
| 14 -From Text to Types - Mastering Structured Output in Spring AI.mp4 | 44.7 MB | ||
| 15 -From AI Text to Java Objects - Spring AI Structured Output Demo.mp4 | 66.6 MB | ||
| 16 -Using Bean, List, and Map Output Converters in Spring AI.mp4 | 50.3 MB | ||
| 17 -Mapping AI Response to ListPOJO using ParameterizedTypeReference.mp4 | 40.6 MB | ||
| 2 -LLMs Forget Everything — ChatMemory to the Rescue!.mp4 | 71.3 MB | ||
| 2 -Meet the Gen AI Family - AI, ML, DL, and Beyond.mp4 | 99.2 MB | ||
| 2 -Understanding Message Roles in LLMs - Demo.mp4 | 72.8 MB | ||
| 2 -What is Spring AI Framework.mp4 | 51.9 MB | ||
| 3 -Funny memes of AI.mp4 | 26.9 MB | ||
| 3 -Hello World Spring AI app with OpenAI - Part 1.mp4 | 91.4 MB | ||
| 3 -Making ChatClient ‘Remember’ – Spring AI’s Memory Advisors.mp4 | 56.2 MB | ||
| 3 -What Are Defaults in Spring AI.mp4 | 90.9 MB | ||
| 4 -From Stateless to Stateful - Spring AI Memory in Action.mp4 | 71.8 MB | ||
| 4 -Hello World Spring AI app with OpenAI - Part 2.mp4 | 62.6 MB | ||
| 4 -Types of Generative AI Models.mp4 | 25 MB | ||
| 4 -Using Prompt Templates in Spring AI.mp4 | 105.6 MB | ||
| 5 -Deep dive on ChatModel and ChatClient.mp4 | 48.2 MB | ||
| 5 -Introduction to Large Language Models - The Text Experts.mp4 | 88.5 MB | ||
| 5 -Per-User Memory in LLMs – Thanks to CONVERSATION_ID!.mp4 | 40.1 MB | ||
| 5 -Using Prompt Stuffing technique.mp4 | 84.9 MB | ||
| 6 -Building a “Hello World” App with Spring AI and Ollama.mp4 | 56.6 MB | ||
| 6 -Chat Memory That Lasts – Spring AI with JDBC-Based Persistence.mp4 | 105.6 MB | ||
| 6 -Core job of an LLM - Guessing next word.mp4 | 56 MB | ||
| 6 -Why Prompt Stuffing Isn’t Meant for Big Data.mp4 | 31.8 MB | ||
| 7 -Building a “Hello World” App with Spring AI and Docker.mp4 | 62.2 MB | ||
| 7 -The concept of Tokens & Token IDs in LLM.mp4 | 56.6 MB | ||
| 7 -Understanding Advisors in Spring AI Workflows.mp4 | 38.4 MB | ||
| 7 -Using maxMessages to Limit Chat History in Spring AI.mp4 | 44.2 MB | ||
| 8 -Avoiding Token Overload - Use maxMessages with Context Window size in Mind.mp4 | 66.2 MB | ||
| 8 -Building a “Hello World” App with Spring AI and AWS Bedrock - Part 1.mp4 | 104.6 MB | ||
| 8 -Inside an LLM’s Dictionary - Understanding Model Vocabulary.mp4 | 59.4 MB | ||
| 8 -Spring AI Built-in Advisors - Plug and Play Intelligence.mp4 | 100.8 MB | ||
| 9 -Building a “Hello World” App with Spring AI and AWS Bedrock - Part 2.mp4 | 71.7 MB | ||
| 9 -Custom Advisors in Spring AI - Make the AI Work Your Way.mp4 | 87.1 MB | ||
| 9 -Embeddings & Vectors - A way to represent meaning.mp4 | 61 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 51 total files | |||
From Java Dev to AI Engineer: Spring AI Fast Track
https://WebToolTip.com
Published 8/2025
Created by Madan Reddy,Eazy Bytes
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 49 Lectures ( 6h 19m ) | Size: 3.11 GB
Build AI Apps with Spring AI, OpenAI, RAG, MCP, AI Testing, Observability, Speech & Image Generation
What you'll learn
Build Spring Boot applications powered by Spring AI
Integrate Spring AI app with OpenAI, Ollama, Docker Model Runner, and AWS Bedrock
Use prompt templates and prompt stuffing techniques
Convert AI text responses to Java Beans, Lists, and Maps
Understand how LLMs work internally with tokens and embeddings
Implement Retrieval-Augmented Generation (RAG) with Spring AI
Implement memory in chat apps using Spring AI advisors
Teach LLMs to call tools exposed by Java methods
Build both MCP clients and servers with Spring AI
From Testing to Production – Making AI Answers Safer with Evaluators
Observability in Spring AI – Metrics, Monitoring & Tracing
Transcription, Speech, and Image Generation using Spring AI
Requirements
Knowledge on Java, Spring Boot is mandatory
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