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Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges.
Large Language Models: The Hard Partsoffers a clear, practical examination of the limitations developers and ML engineers face when building LLM-powered applications. With a focus on implementation pitfalls (not just capabilities) this book provides actionable strategies supported by reproducible Python code and open source tools.
Readers will learn how to navigate key obstacles in system integration, input management, testing, safety, and cost control. Designed for engineers and technical product leads, this guide emphasizes practical solutions to real-world problems and promotes a grounded understanding of LLM constraints and trade-offs.
- Design testing strategies for nondeterministic systems
- Manage input formatting and long-context retrieval
- Address output inconsistency and structural unreliability
- Implement safety and content moderation frameworks
- Explore alignment challenges and mitigation techniques
- Leverage open source models and optimize costs
About the author
Dr. Tharsis Souza is a computer scientist and product leader specializing in AI-based products. He is a Lecturer at Columbia University's Master of Science program in Applied Analytics, Head of Product, Equities at Citadel, and former Senior VP at Two Sigma Investments. With over 15 years of experience delivering technology products across startups and Fortune 500 companies globally, Dr. Souza is also an author of numerous scholarly publications and is a frequent speaker at academic and business conferences. Grounded on academic background and drawing from practical experience building and scaling up products powered by language models at early-stage startups, major institutions as well as advising non-profit organizations, and contributing to open source projects, he brings a unique perspective on bridging the gap between LLMs promised potential and their practical limitations using open source tools to enable the next generation of AI-powered products. Dr. Tharsis holds a Ph.D. in Computer Science from UCL, University of London following an M.Phil. and M.Sc. in Computer Science and a B.Sc. in Computer Engineering.
