# LLM Engineer’s Handbook: LLM Engineering from Concept to Production

Concept: LLM Engineer’s Handbook is a practical guide to building and deploying large language model applications, with coverage spanning data engineering, retrieval-augmented generation, fine-tuning, evaluation, and LLMOps. Its project-oriented focus on an LLM Twin and production workflows is aimed at readers ready to apply Python and AWS knowledge.

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- Price: 35.23 USD
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- Rating: 4.6 / 5
- Reviews: 247

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## At a glance

- Works through an LLM Twin use case, connecting practical model work with production-system design.
- Topics include data pipelines, RAG feature and inference pipelines, supervised fine-tuning, and preference alignment.
- Covers LLM evaluation, inference optimization, deployment, and MLOps or LLMOps practices.
- The contents include deployment using AWS and other tools.

## Pros

- Broad coverage links model preparation and refinement with evaluation and deployment rather than focusing only on isolated notebooks.
- The LLM Twin project provides a through-line for exploring modular systems and hands-on implementation.

## Specifications

### Book details
- Publisher: Packt Publishing
- Publication date: October 22, 2024
- Language: English
- Print length: 522 pages
- ISBN-10: 1836200072
- ISBN-13: 978-1836200079
### Dimensions and installation
- Weight listed: 2.1 pounds
- Dimensions: 7.5 x 1.18 x 9.25 inches

## Before you buy

- The publisher recommends basic familiarity with LLMs and generative AI, Python, and AWS. The guide’s coverage should not be taken as a substitute for prior practical setup experience.

## A good fit for

- AI engineers, NLP professionals, and LLM engineers seeking a hands-on path through LLM application development and production workflows.
- Readers comfortable with Python and AWS who want to explore RAG, fine-tuning, evaluation, and LLMOps in one book.

## Less suited to

- Readers looking for a purely introductory AI overview without technical prerequisites or implementation work.
- Buyers seeking only a focused reference on a single topic such as prompt engineering or RAG.

## Customer feedback

The supplied evidence includes aggregate star ratings but no customer-comment summary. The distribution is weighted toward five-star ratings, with a smaller share of lower ratings, but it does not establish what readers liked or disliked. Consider that context alongside the book’s substantial coverage and stated technical prerequisites, which may affect how useful it is to different readers.

## Frequently asked questions

### What topics does the book cover?
The listing describes data engineering, RAG pipelines, supervised fine-tuning, preference alignment, evaluation, inference optimization, deployment, and LLMOps.
### Does the book require prior technical knowledge?
The listing recommends basic knowledge of LLMs and generative AI, Python, and AWS.
### Is AWS included in the deployment material?
Yes. The listing says the book covers deploying end-to-end LLM solutions using AWS and other tools.

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## Add-ons

- [AI Engineering: Building Applications with Foundation Models](https://best-deals.pro/visit/llm-engineers-handbook-large-language-models)
- [Build a Large Language Model \(From Scratch\)](https://best-deals.pro/visit/llm-engineers-handbook-large-language-models)

## Images

![LLM Engineer’s Handbook: LLM Engineering from Concept to Production — 1](https://best-deals.pro/i/epppAimcwEe84J4wzTTG7m_Kwakj7j169E0V3qzHJp91NIm4It9Ww37_wdnHhg2or_xjc-aERCeAkyp84f4Dk0-A0K1x-WDjkZmaavZd7m0-X3MwICbb.jpg)