Spring AITraining program
Spring AI
Build intelligent Java applications with the Spring AI framework — real LLM integrations, RAG pipelines, and AI microservices.
Overview
Learn to integrate large language models into Java/Spring applications using Spring AI: prompts, structured output, vector search, and RAG. You’ll build services that are testable and deployable, not one-off scripts.
Top features
- Configure Spring AI against major model providers and manage prompts in code.
- Implement retrieval-augmented generation with embeddings and a vector store.
- Expose AI capabilities via REST and integrate with your existing Spring Boot services.
- Apply production-minded patterns: errors, timeouts, evaluation basics, and deployment.
Key highlights
Skills under this course
Course curriculum
Weekly structure may shift slightly by cohort; this is the core syllabus we cover end to end.
Spring AI foundations
- Providers + configuration
- Prompt templates + structured output
- Function calling patterns
- Prompt safety + token optimization
- Model selection strategies
Prompt engineering for Java teams
- Prompt patterns by use case
- Template parameterization strategy
- Prompt versioning basics
Model integration architecture
- Provider abstraction in Spring AI
- Fallback model strategy
- Latency and token budgeting
RAG pipelines
- Embeddings + document loading
- Chunking strategies
- Vector store integration + retrieval quality
- Reranking and context assembly
- Hybrid retrieval concepts
RAG quality and evaluation
- Retrieval precision/recall basics
- Chunking and overlap tuning
- Response quality measurement loops
Security and compliance for AI APIs
- Input sanitization and prompt injection basics
- PII masking patterns
- Audit logging and data boundaries
Production readiness
- Timeouts, retries, fallbacks
- Evaluation basics + guardrails
- Deploying AI microservices
- Monitoring prompts and responses
- Cost/performance tuning for LLM apps
Operational excellence for AI services
- Observability for LLM calls
- Rate limiting and backpressure
- Incident handling for model outages
Capstone planning and delivery
- Problem framing and architecture
- Milestones and review checkpoints
- Demo narrative for interviews
Real world case studies & projects
- Guided Spring AI use-case implementation
- Architecture and troubleshooting review with mentor
- Portfolio-ready mini project with code walkthrough
Reviews
*****
“Clear structure, practical assignments, and mentor guidance made Spring AI easy to apply at work.”
TCS
*****
“Weekly feedback and project checkpoints helped me stay consistent and interview-ready.”
Infosys
*****
“Mentor sessions were practical and direct. I gained confidence in architecture decisions and implementation trade-offs.”
Cognizant
*****
“Structured curriculum, clear feedback, and consistent support helped me complete projects faster than expected.”
Accenture
Course FAQs
Do I need prior experience?
This track is marked Intermediate. We support learners from adjacent backgrounds and share a prep path in the first week.
Are sessions live or recorded?
Sessions are live and interactive. You also get recordings for revision and catch-up.
How much weekly time should I plan?
Typical effort is 6-8 hours/week including classes, labs, and revision for this 8 Weeks program.
Will I build real projects?
Yes. Each cohort includes practical case studies and implementation-focused assignments.