This early hands-on book matters less as a map of today's stack and more as a snapshot of the first real engineering skeleton around GPT applications.
The chapter breaks down how OpenAI APIs, prompt engineering, baseline prompt-injection defenses, and early approaches to fine-tuning and orchestration came together as the starter kit for the first generation of LLM applications.
In interviews, it is a good way to show how the practice evolved from directly calling a model to building more mature systems with control over context, safety, and behavior.
Practical value of this chapter
LLM entry point
The chapter shows the minimum useful path: API usage, prompt design, light safeguards, and first product scenarios.
Historical snapshot
It is a convenient way to explain what the first engineering skeleton around GPT apps looked like before more mature patterns appeared.
Practice evolution
The book makes it easy to show how the path from demo to working system gradually runs into evaluation, safety, and cost control.
Interview material
It is a useful case for explaining how LLM app practice evolved from direct API calls to more mature architecture.
Source
Telegram: Book Cube
Review post with a practical take on what is still useful and what is already outdated.
Developing Apps with GPT-4 and ChatGPT
Authors: Enamul Haque, Douglas Murillo, S. M. Nasrul Quader, Benjamin Pyle, Steve Tingiris
Publisher: O'Reilly Media, 2023 (1st Edition)
Length: 125 pages
A concise 2023 hands-on guide to the first generation of LLM applications: OpenAI API basics, prompting, prompt-injection mitigation, fine-tuning, and early LangChain patterns.
What this book is about
This is a short hands-on 2023 book that shows the starter path: understand LLM basics, connect the API, and assemble first useful scenarios without heavy theory overhead.
It still works well as an onboarding resource, but it is a starting point, not a current map of the full GenAI stack: the ecosystem has moved far ahead in the intervening years. The gap shows up most in sections on fine-tuning, early orchestration, and runtime reliability.
Book structure: 5 short chapters
1. GPT-4 and ChatGPT overview
- A short introduction to NLP and why transformers became the default architecture for modern LLMs.
- Historical baseline: from Attention Is All You Need to the GPT model line.
- Early product examples from education, finance, and media.
2. ChatGPT API deep dive
- How to create an API key, use the OpenAI Playground, and call models from Python.
- Request/response format and core generation parameters.
- Pricing basics and entry-level security considerations for integration.
3. Building simple applications
- Hands-on demos and how to shape model input and output to reduce prompt-injection risk.
- The same model setup is used to build multiple lightweight app scenarios.
- A practical bridge from Playground experiments to a first working product.
4. Prompt engineering and fine-tuning
- Step-by-step prompting, few-shot patterns, and task decomposition.
- Introduction to OpenAI fine-tuning and cost implications.
- Practical sample: email marketing text generation.
5. LangChain and plugins (historical snapshot)
- Shows an early stage of the ecosystem: LangChain plus ChatGPT plugins.
- From a 2026 perspective, this section is valuable as an evolution snapshot toward GPTs.
- Read it for how tools and orchestration patterns evolved, not as a guide to follow today.
What remains useful
- Low entry barrier: the book takes a reader from zero to a first LLM application in plain language.
- The concepts -> API -> small demos progression is laid out so that little time passes between reading and a first request.
- Covers just enough of the prompt-engineering baseline to assemble a first working scenario without drowning in detail.
What is outdated or incomplete
- Parts of the API and model lineup are outdated compared with the 2026 landscape — code from the book has to be checked against current docs.
- The plugin section reflects a transitional pre-GPTs product era.
- Evaluation, guardrails, and reliability in live systems are covered only at a basic level — that minimum is not enough for production.
Demo apps covered in the book
- News generator
- YouTube transcript-based summarizer
- Video game lore assistant
- Basic voice control interface
Recommended companion reading
The book gets you to a first demo but stops where production begins. To close that gap, pair it with newer materials on evaluation, guardrails, context engineering, and agent patterns.
Foundational references used in this learning path: GPT / Generative pre-trained transformer and Attention Is All You Need.
For prompt safety in real systems, add current controls from OWASP LLM Top 10.
Related chapters
- Prompt Engineering for LLMs - Modern prompt/context-engineering patterns and practical LLM workflow design.
- Hands-On Large Language Models - Deeper coverage of RAG, embeddings, agent patterns, and LLM infrastructure choices.
- AI Engineering - What to add to this book's demos to reach a working system: reliability, evaluation, and cost management.
- OWASP Top 10 in System Design - How to close the book's security gap: prompt-injection risks and baseline controls for GenAI applications.