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
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 9 contributors on GitHub.
Gained 53% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a comprehensive, end-to-end framework for building, training, evaluating, and deploying large language models (LLMs) and retrieval-augmented generation (RAG) systems using best practices.
- Integrates ZenML pipelines, AWS SageMaker deployment, and monitoring tools to streamline LLMOps workflows from data collection to production-ready inference with modular, maintainable code.
- Use for developing and fine-tuning custom LLMs with automated data pipelines and training orchestration on AWS SageMaker for scalable model training and evaluation.
- Use for deploying production-grade LLM inference endpoints on AWS with integrated monitoring and prompt tracking using Comet ML and Opik for real-time API serving.
- Use for experimenting with RAG systems combining vector search (Qdrant) and LLMs, leveraging MongoDB for data warehousing and ZenML for reproducible ML workflows.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
Watch-outs
- No commits in the last six months (last push Apr 2026).
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
Official repository of the LLM Engineer's Handbook by Paul Iusztin and Maxime Labonne
Find the book on Amazon or Packt
The goal of this book is to create your own end-to-end LLM-based system using best practices:
You can download and use the final trained model on Hugging Face.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
1.3K+60
Open issues
36+2
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| PacktPublishing/LLM-Engineers-Handbook | 5.4K | — | 10 | — |
| Category median | 14.7K | +233 | 63 | — |
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