AI for Java Developers (Recording)

Beginner Friendly | Using Spring AI (Recorded)

🎙 Main Speaker

1. Sumit

🎤 Guest Speakers

1. Vaibhav
2. Ritesh

What We’ll Cover Today

1. Why AI matters for Java devs
2. Important AI terms
3. What is Spring AI
4. RAG explained simply
5. Production basics

Why Should Java Devs Care About AI?

1. AI is becoming a product feature
2. Chatbots, smart search, summaries
3. Companies expect backend integration
4. You don’t need ML knowledge to start

What is an LLM?

1. Large Language Model
2. Predicts next word (token)
3. Example: GPT models
4. It does not “think” like humans

Important AI Terms (1)

1. Token – smallest unit of text
2. Prompt – instruction to model
3. Temperature – randomness control
4. Inference – calling the model

Important AI Terms (2)

1. Embeddings – text converted to numbers
2. Vector – list of numbers
3. Vector Database – similarity search
4. Hallucination – confident wrong answer

What is Spring AI?

1. Spring abstraction over AI providers
2. Works like Spring Data style
3. ChatClient & EmbeddingClient
4. Easy provider switching

Simple Architecture Flow

User → Controller

Controller → Service

Service → Spring AI

Spring AI → LLM → Response

What is RAG?

1. Retrieval Augmented Generation
2. Adds your company data to prompt
3. Reduces hallucination
4. Used for internal chatbots

RAG Flow (Simple Steps)

1. Store documents
2. Generate embeddings
3. Store in vector DB
4. Search similar content + inject

Production Concerns

1. Latency (AI is slow)
2. Retry & circuit breaker
3. Rate limiting
4. Monitor token cost

Final Takeaway

1. You don’t need to train models
2. You need to integrate properly
3. Start small with one feature
4. Spring AI makes it simple