5 Free Must-Read Books Every AI Engineer Should Dive Into for 2025 Mastery

Artificial Intelligence engineering is rapidly evolving, requiring continuous learning beyond traditional coursework. In 2025, experts recommend five free, in-depth books that blend theory, hands-on practice, and governance—all crucial for AI engineers aiming for a competitive edge.

Foundations of Large Language Models by Tong Xiao & Jingbo Zhu explains the architecture and training of models like GPT and LLaMA, focusing on clarity over code snippets.

Speech and Language Processing (3rd Edition Draft) by Daniel Jurafsky & James H. Martin offers a comprehensive update on NLP, covering embeddings, transformers, and retrieval augmented generation.

How to Scale Your Model: A Systems View of LLMs on TPUs (Google AI Research) dives into the system-level challenges of training trillion-parameter models efficiently.

Understanding Large Language Models: Interpretability and Self-Rationalisation by Jenny Kunz examines model transparency using probing classifiers.

Large Language Models in Cybersecurity: Threats, Exposure, and Mitigation addresses AI security issues as LLMs integrate into business workflows.

These books empower AI engineers with foundational insights, systems design mastery, and ethical awareness critical for future-proofing careers.

Key Highlights:

Covers theory and application beyond mere coding tutorials.

Emphasizes model architecture, training, interpretability, and security.

Suitable for AI engineers, data scientists, and NLP practitioners.

Resources freely available in 2025, reflecting latest AI advances.

Balances foundational knowledge with practical deployment strategies.

Sources: KDnuggets, LinkedIn, Towards AI, The Neural Maze