From Java To Ai: The Python-Free Guide To Ai And Llms

A Complete Code-Free Introduction to Core LLM concepts

What you'll learn

The fundamentals of AI and LLMs explained in simple, accessible language without mathematical complexity

How modern language models actually work under the hood, demystified for the Java developer

Text processing and tokenization explained with practical implications for your applications

Core capabilities of LLMs and what they can (and cannot) do for your Java projects

How to run Large Language Models and use them locally

Step-by-step integration of LLMs into Java applications using standard HTTP clients and JSON processing

Crafting effective prompts that produce consistent, reliable results for business applications

Managing contextual conversations with stateless LLMs through proper session handling

Tuning LLM parameters to control creativity, response length, and output variety

Advanced prompting techniques that improve response quality without requiring AI expertise

Structuring LLM outputs for seamless parsing and type safety in Java applications

Error handling strategies for dealing with the unique challenges of AI-generated content

Properly formatting system messages to control AI behavior consistently

Context window optimization to handle lengthy interactions efficiently

Validating and verifying LLM responses to prevent incorrect or hallucinated information

Zero-to-hero understanding of LLMs that lets you join AI discussions with confidence - no Python required!

Requirements

Familiarity with Java programming

Basic consumer laptop / desktop. No beefy hardware required

Curiosity about AI and LLMs

No Python knowledge required

No machine learning or statistics background needed

No advanced mathematics required

Description

Feel left behind by the AI revolution because you don't know Python? This course is your gateway to the exciting world of Large Language Models, specifically designed for Java developers who want to understand and implement AI without changing their tech stack.Through clear, jargon-free explanations, you'll discover how LLMs actually work - from their fundamental architecture and tokenization to sophisticated prompting techniques and integration patterns. We explore everything from the basics of what AI truly is to advanced concepts like context windows, structured outputs, and error handling strategies.The curriculum methodically builds your knowledge: starting with AI and LLM foundations, moving to practical Java integration with HTTP, then advancing to conversation management, prompting patterns, and robust error handling. Each concept is explained in plain English with practical insights.We've eliminated the traditional barriers to AI learning - no complex mathematics, no Python requirements, and no machine learning prerequisites. This course teaches you all the essential concepts of AI in an accessible way that lets you experience those rewarding "aha!" moments as concepts click into place.By the end, you'll have transformed from feeling left out of the AI conversation to being equipped with practical knowledge to confidently integrate these powerful tools into your Java applications right away.

Overview

Section 1: Foundations of AI and Machine Learning

Lecture 1 What is AI really?

Lecture 2 How is AI Different From Traditional Software?

Lecture 3 The BEST Explanation of AI Training

Lecture 4 Challenges and Pitfalls in AI Training

Lecture 5 AI vs. Machine Learning - What's The Difference?

Section 2: Understanding Language Models

Lecture 6 What are Language Models?

Lecture 7 What does a model look like?

Lecture 8 Why "large" language models?

Lecture 9 Understanding typical sizes of LLMs

Lecture 10 Training an LLM - what actually gets adjusted?

Lecture 11 What about conflicts in training?

Lecture 12 LLM size - Is more always better?

Section 3: Text Processing and Tokenization

Lecture 13 How LLMs process text

Lecture 14 How big are tokens?

Lecture 15 Tokenizer types

Lecture 16 Tokenizer Visualizer Demo

Section 4: Capabilities and Integration

Lecture 17 Four Common Capabilities of LLMs

Lecture 18 Integration for Java developers

Section 5: Prompting and Interaction with LLMs

Lecture 19 Prompting and how it works

Lecture 20 The rationale behind prompting

Lecture 21 Chain of thought prompting

Lecture 22 Single-turn and Multi-turn interactions

Lecture 23 LLMs are stateless

Section 6: Context Management and Structured Messaging

Lecture 24 Tokens and Context Windows

Lecture 25 Context window precision and tradeoffs

Lecture 26 Structured Messages - System, Assistant and User

Lecture 27 Some examples of structured messages

Lecture 28 Context window truncation - Bye bye system message?

Lecture 29 Questions about system message answered

Section 7: LLM Configuration and Robust Output Handling

Lecture 30 LLM Configuration parameters

Lecture 31 The temperature parameter

Lecture 32 Max tokens parameter

Lecture 33 Top K and Top P sampling

Lecture 34 Structured outputs

Lecture 35 Tool calling

Lecture 36 Handling Errors and Unexpected Outputs

Lecture 37 Retry strategies

Lecture 38 Validation

Java developers who feel left behind by the AI revolution and want to catch up quickly,Software engineers who want to understand AI/LLM concepts without switching to Python,Backend developers looking to enhance Java applications with AI capabilities,Technical professionals who need to understand AI terminology and capabilities for work discussions,Java programmers curious about the fundamentals of how LLMs actually work,Developers who learn better through clear explanations rather than mathematical formulas,Software architects evaluating how to integrate LLMs into existing Java-based systems,Engineers who want practical knowledge they can apply immediately in familiar environments