The chapters cover a range of topics, including structured output challenges, input and output size limitations, evaluation gaps, hallucination issues, safety concerns, cost factors, and vendor lock-in problems. Each chapter offers strategies, techniques, and tools to tackle these challenges, with a focus on practical implementation and best practices. The book also includes an appendix with tools and resources, making it a valuable resource for those looking to build robust LLM-powered applications.
Key takeaways:
- The book critically examines the limitations and challenges engineers face when implementing LLM-powered applications, offering practical solutions through Python examples and open source tools.
- It addresses key issues such as handling unstructured output, managing context windows, and overcoming input and output size limitations.
- Chapters cover a range of topics including structured output, hallucination detection, safety concerns, cost optimization, and breaking free from cloud provider dependencies.
- The guide is designed for engineers and technical product managers, providing them with the knowledge to effectively navigate LLM pitfalls and build robust applications.