Hands-On Large Language Models versus Natural Language Processing with Transformers.
Both show up on every "best" list. They're not competitors. They're a sequence. Here's which one to read first, and when.
Reviewed by Ashish Sheth · Updated May 2026
Option B
Natural Language Processing with Transformers
Lewis Tunstall, Leandro von Werra, Thomas Wolf · 2022
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Author
Jay Alammar, Maarten Grootendorst
Lewis Tunstall, Leandro von Werra, Thomas Wolf
Pages
425
408
Published
2024
2022
Publisher
O'Reilly Media
O'Reilly Media
Level
beginner to intermediate
intermediate
Amazon Rating
4.5/5 (392)
4.6/5 (257)
Goodreads Rating
4.29/5 (254)
4.39/5 (212)
Hands-On Large Language Models
Strengths
+ 275+ custom diagrams make abstract concepts visual and intuitive
+ Accessible to beginners without prior PyTorch/TensorFlow knowledge
+ Practical code examples covering real use cases like semantic search and RAG
+ Well-structured progression from foundations to advanced techniques
Caveats
− Limited depth on transformer internals despite the author's blog reputation
− Image generation sections lack clarity
− May be too introductory for experienced ML practitioners
Natural Language Processing with Transformers
Strengths
+ Written by the team that built the Transformers library — definitive authority
+ Code-first with runnable Hugging Face examples throughout
+ Covers the full lifecycle: pretrain, fine-tune, evaluate, deploy
+ Excellent for engineers who want to actually ship NLP features
Caveats
− Predates the LLM/GPT-4 era — emphasis is on smaller fine-tuned models
− Some library APIs have evolved since publication
− Less coverage of generative LLMs than the title implies for 2026 readers
The verdict
Read Hands-On Large Language Models first to build foundations, then move to Natural Language Processing with Transformers for advanced concepts.
Hands-On Large Language Models
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Natural Language Processing with Transformers
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Frequently asked
Which is better, Hands-On Large Language Models or Natural Language Processing with Transformers?
Read Hands-On Large Language Models first to build foundations, then move to Natural Language Processing with Transformers for advanced concepts.
Is Hands-On Large Language Models good for beginners?
Yes, it's one of the most beginner-friendly LLM books available. No PyTorch or TensorFlow experience needed. The 275+ diagrams carry a lot of the explanation load.
Is NLP with Transformers still relevant given how fast LLMs evolved?
The transformer architecture chapters and Hugging Face workflows are still the standard. What's outdated: emphasis on BERT-era fine-tuning over GPT-style prompting. Pair with a current LLM book.