توضیحات
Written by James Phoenix, this book explores both the art and science behind crafting precise, efficient, and context-aware prompts that drive LLMs to produce accurate, reliable, and creative outputs. It is designed for software engineers, AI enthusiasts, and researchers who want to integrate LLMs into real-world applications.
The book blends theory with practical examples, showing how prompt engineering forms the foundation for building intelligent, adaptive, and productive AI-powered systems.
Key topics include:
Foundations of Prompt Engineering: Understanding how LLMs interpret, process, and generate text.
Types of Prompts: Zero-shot, few-shot, and chain-of-thought prompting.
Designing Effective Prompts: Structuring inputs to achieve clarity, context, and precision.
Prompt Optimization: Iteratively refining prompts to improve model accuracy and consistency.
Controlling Output Behavior: Using constraints, roles, and formatting instructions to guide model responses.
Prompting for Coding and Data Tasks: Enhancing developer productivity in Python, SQL, and API workflows.
Advanced Techniques: Function calling, embeddings, and model fine-tuning strategies.
Integration with Applications: Building LLM-powered apps using frameworks like LangChain, OpenAI API, and Hugging Face.
Ethics and Safety: Ensuring responsible use of AI outputs and preventing hallucinations or bias.
Real-World Projects: Case studies on chatbots, documentation assistants, and generative content tools.
By mastering the principles in this book, readers will be able to design intelligent prompts, integrate LLMs into software systems, and leverage AI for innovation, automation, and decision-making across industries.
نقد و بررسیها
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