build-your-own-ai
Build Your Own AI is a developer-focused book by Sebastian Wessel that provides practical, language-agnostic guidance (illustrated in TypeScript) for building real-world AI applications, covering LLMs, RAG, tool calling, agents, finetuning, and more.
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build-your-own-ai
Build Your Own AI is a practical developer’s guide that teaches how to build real-world AI applications. Presented by a developer for developers, the book emphasizes core concepts and patterns that can be applied across programming languages while illustrating examples in clean, framework-free TypeScript. It is positioned for absolute beginners, developers with some knowledge, and enthusiasts — explicitly noting it is not designed for scientists.
The book offers a full preview of chapters and summaries on the site, and is available to purchase from the author’s store. Topics span running LLMs locally, prompt engineering, data extraction and creation, retrieval-augmented generation (RAG) with vector databases and document retrieval, tool calling, agents, and basics of fine-tuning, plus tips and tricks for practical use.
A practical guide for developers to build real-world AI applications with example code.
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Developer-focused guidance
A guide written by a developer for developers that reflects the author’s learning process and targets practical application rather than academic research.
Language-agnostic approach
Core concepts and patterns are taught so readers can apply the material in whatever programming language they prefer.
Framework-free TypeScript examples
Illustrative examples are provided in clean TypeScript without frameworks or libraries, using simple HTTP requests to demonstrate ideas.
Full preview available
The entire book can be previewed on the site with chapter summaries to help readers understand the content before purchase.
Coverage of practical AI topics
Topics include running LLMs locally, prompt engineering, data extraction & creation, RAG, tool calling, agents, basics of fine-tuning, and practical tips and tricks.
Store purchase
The book is available to buy from the author’s store (purchase links provided on the site).
Pricing
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Use Cases
Learn to run LLMs locally
Guidance on running large language models on local machines to gain control, privacy, and potentially improved performance.
Implement Retrieval-Augmented Generation (RAG)
Instructions for integrating vector databases and document retrieval to expand model capabilities and provide context-rich responses.
Build tool-calling functionality
Approaches to adding external tools to AI systems to improve functionality, efficiency, and personalization.
Create and coordinate agents
Techniques for constructing multi-step or multi-component agent-based scenarios to handle complex workflows.
Prompt engineering and data tasks
Methods for crafting prompts and using LLMs for extraction, summarization, translation, and other data creation tasks.
Explore fine-tuning basics
A high-level introduction to fine-tuning models for specific tasks, outlining general approaches and requirements.
Integrations
Vector databases
Discussed as part of RAG to store and retrieve embeddings for context-rich responses.
Document retrieval systems
Mentioned in the context of integrating document retrieval with LLMs to improve accuracy and context.
Benefits
Limitations
Frequently Asked Questions
Who is this book for?
Do I need to learn Python or JavaScript/TypeScript?
Getting Started
- 1 Preview the book on the site to read chapter summaries and sample content.
- 2 Purchase the full book from the author’s store if you want the complete guide.
- 3 Follow the framework-free TypeScript examples and core concept chapters, applying the patterns to the programming language you prefer.
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