# PacktPublishing/LLM-Engineers-Handbook

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

Repository: https://github.com/PacktPublishing/LLM-Engineers-Handbook
Canonical: https://ross.abutalabs.com/products/llm-engineers-handbook
Homepage: https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/
Language: Python
License: MIT
License Family: permissive
Topics: genai, llm, llmops, mlops, rag, aws, fine-tuning-llm, llm-evaluation, ml-system-design
Last push: 2026-04-22T08:25:03+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 78, release rhythm 35, longevity 62
- inputs: {"age_days": 876, "days_push": 133, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5296, forks 1285 (observed 2026-08-28T04:09:15.209453+00:00)

## What it is
The official companion repository for the book 'LLM Engineer's Handbook' by Paul Iusztin and Maxime Labonne, containing Python code for building an end-to-end LLM-based system. It covers data collection, LLM training, a RAG system, AWS deployment, monitoring, and evaluation following LLMOps best practices.

## Use cases
- learn llm engineering from fundamentals to production
- build and deploy a rag application on aws
- fine-tune a llama model with dpo
- set up llm training and evaluation pipelines
- learn llmops best practices for monitoring and deployment
- study an end-to-end ml system design example

## When to choose
- you are reading the LLM Engineer's Handbook and want the latest maintained code
- you want a hands-on, end-to-end project covering LLM training, RAG, and AWS deployment
- you want to learn LLMOps practices like monitoring, testing, and evaluation

## When to avoid
- you need a production-ready library or framework to drop into your own project
- you want a tool without needing to follow the accompanying book
- you are not working in Python or on AWS

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, rag, machine-learning, monitoring, testing, etl
- domain: large-language-models, machine-learning, tutorials, cloud-computing
- platform: python, cloud
- tags: llmops, mlops, book-companion, fine-tuning, llm-evaluation, aws-deployment, hands-on-project, retrieval-augmented-generation, devops, docker

## Member repositories
- PacktPublishing/LLM-Engineers-Handbook (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:15.209453+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:59:10.320402+00:00, confidence not recorded.
  - readme: https://github.com/PacktPublishing/LLM-Engineers-Handbook (fetched 2026-08-28T04:09:15.209453+00:00, sha f27d72d8e8ab)
  - homepage: https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/ (fetched 2026-08-29T08:53:56.263880+00:00, sha 6d1d7ac1af17)
- Data as of 2026-08-30T08:39:29.467469+00:00.