AI Systems, Explained makes these concepts easier to understand through simple explanations, practical examples, and easy-to-remember mental models.
In this book, you will explore:
- How modern AI models and Large Language Models (LLMs) work
- Tokens, context windows, inference, reasoning, and multimodal AI
- Prompt engineering and how to communicate effectively with AI
- Context engineering and how to provide AI with the right information
- AI memory and the difference between memory, context, knowledge, and training
- RAG, embeddings, vector databases, retrieval, and reranking
- How AI search combines search, retrieval, ranking, and language models
- How AI agents use tools, planning, memory, and decision-making to complete tasks
- The difference between workflows, agents, and multi-agent systems
- Why AI systems hallucinate, fail, and sometimes produce confident but incorrect answers
- How the different pieces come together to form modern AI systems
This book is written for everyone-from students and beginners with no AI background to software engineers, product professionals, and experienced technology practitioners who want to understand modern AI more deeply.
You do not need a background in machine learning or artificial intelligence to get started.
Instead of focusing only on how to use AI tools, this book helps you understand what is happening behind the interface.
By the end, you will have a clear mental model of how models, prompts, context, memory, knowledge, search, tools, and agents work together.
This is Book 1 in the AI Systems, Explained series, focusing on the foundations of modern AI.
AI is not just a model. It is a system.
And understanding that system is the first step toward building, using, and evaluating AI more effectively.
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