Generative AI Course for Java and Software Engineers

The gap between a working prototype and a feature you are willing to put in front of paying users — and the specific engineering that closes it.

For backend engineers asked to add a generative feature to an application that already has users, uptime expectations and a bill.

What you will be able to do

• Wire generation into an existing Spring Boot application without making every request depend on a third party staying up.
• Stream a response to a browser and handle the half of the failure cases that only exist once output is incremental.
• Cache generated content correctly — knowing which requests are safe to cache and which two are not the same request at all.
• Put a cost ceiling on a feature before it ships, and measure cost per successful outcome rather than per call.
• Degrade honestly when the model is unavailable, instead of showing a user an error that blames them.

Curriculum

LLM Foundations

• What Is a Large Language Model?
• LLM Strengths & Limitations
• Tokens: The Basic Unit of LLM Text
• Tokens: The Basic Unit of Text
• How LLMs Read Text (Tokenization)
• Tokenization & Encoding Schemes
• Embeddings vs Tokens
• Context Windows
• Context Windows: Capacity & Limits
• Next-Token Prediction
• Next-Token Prediction & Probability Distributions
• Probability Distributions
• Temperature
• Top-k & Top-p Sampling
• Why LLMs Hallucinate

Structured Output & Validation

• Why JSON Matters
• Schema-First Generation
• Output Validation in Production Pipelines
• Retry Strategies
• Guardrails
• Production Reliability

Production AI Systems

• Production Architecture
• Latency
• Streaming
• Caching
• Cost Optimization
• Model Gateways
• Fallback Models
• Observability

A lesson from this track

Streaming changes what failure looks like. A request that fails before the first token is an error you can handle; a request that fails after two hundred tokens have already reached the user is a product decision you have to have made in advance.

Streaming

What you build

• A Spring Boot generation endpoint with streaming, backpressure and a timeout that cancels the upstream call rather than orphaning it.
• A caching layer for generated content with a cost dashboard, showing spend per feature and per successful outcome.

Questions

How is this different from the LLM track?

The LLM track is about the model's behaviour — tokens, context, prompting, structured output. This one is about the application around it: integration, streaming, caching, cost and failure handling. Engineers shipping a feature usually want both, and there is a reason they are ordered that way.

Does it assume Spring Boot specifically?

The runnable code is Spring Boot, because that is what the reference implementations are written in. The engineering — timeouts, idempotency, cache keys, cost accounting — transfers to any stack, and the lessons state the principle before the code.

Is there anything on image or audio generation?

The production engineering here — streaming, cost ceilings, caching, degradation — applies to any generative modality, and the cost material uses a real text-to-speech pipeline as its worked example. The lesson content itself is text-model focused.

Can I use this to justify a build-vs-buy decision?

That is largely what the production module is for. It puts real numbers on latency, cost per outcome and the operational surface you take on, which is the part usually missing from that argument.

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