Gen AI with Java Spring Boot and React: concepts and vocabulary | Complete Generative AI: From Foundations to Production
A self-paced 26-week learning path with 15 modules: AI foundations, Python, machine learning, transformers, LLMs, prompts, APIs, embeddings, RAG, frameworks, agents, customization, multimodal AI, Spring Boot with React, and production LLMOps. Includes 15 practical assignments and three portfolio projects.
Prerequisites: Begin with basic computer skills. Programming is introduced in Weeks 2–3. Use a local Python environment for coding labs; Java and React experience helps in Weeks 21–22. Model downloads, provider accounts and deployment resources are optional learner-managed dependencies. The 26-week schedule is suggested; reading estimates exclude independent project work.
Learning goals
Spring AI and model integration concepts
Spring Boot AI service architecture
React chat interface
Streaming responses to React
JWT authentication and permissions
AI resume scoring and job matching
Logging, usage quotas and cost controls
Understand the foundations
A full-stack AI application separates presentation, identity, business policy and provider integration. React manages display, input and cancellation; Spring Boot authenticates the caller, validates requests and invokes an application service. A Spring AI ChatClient adapter can isolate provider calls behind a stable interface. Match the Spring AI dependency version and configuration to the project dependency platform rather than pasting a current sample into an older Spring Boot application without compatibility checks.
Connect the ideas
JWT authentication establishes a verified identity; every resume and analysis lookup must still check object ownership and role. A React route guard is only a navigation convenience. Keep provider credentials and system instructions on the server. Reserve quotas before generation to bound concurrent usage, reconcile actual usage and use structured logs with trace IDs. A resume score should identify its rubric and evidence and remain advisory, because a single number can hide missing data and unfair assumptions.
Checkpoint: explain these concepts in your own words, then describe one failure that the design must detect. Use synthetic or permissioned data throughout the course.
01. Introduction to Artificial Intelligence · Week 1
02. Python Programming for Gen AI · Weeks 2–3
03. Mathematics and Machine Learning Foundations · Week 4
04. Deep Learning and Transformers · Weeks 5–6
05. Large Language Models (LLMs) · Weeks 7–8
06. Prompt Engineering · Weeks 9–10
07. Generative AI APIs and Application Development · Week 11
08. Embeddings and Vector Databases · Weeks 12–13
09. Retrieval-Augmented Generation (RAG) · Weeks 14–15
10. LangChain, LlamaIndex and AI Frameworks · Week 16
11. AI Agents and Agentic AI · Weeks 17–18
12. Fine-Tuning and Model Customization · Week 19
13. Multimodal Generative AI · Week 20
14. Gen AI with Java Spring Boot and React · Weeks 21–22
15. Production Gen AI, LLMOps and Capstone · Weeks 23–26