Manikandan R

This book explains the principles, structure, and real-world practice of prompt engineering in a way that anyone working with large language models can understand and apply. Rather than treating prompting as a collection of hacks, it walks the reader through how to think about role, context, task, constraints, and examples so that LLMs produce reliable, domain-appropriate outputs. Starting from the basics of “what is a prompt,” the book then connects prompting to how generative AI models actually work, introduces core and advanced prompting patterns (prompt chaining, dynamic templates, tool-augmented prompting), and shows how to evaluate and refine model responses. Designed for students, educators, and practitioners, each chapter includes learning objectives and hands-on exercises that can be tried directly in tools like ChatGPT. Later chapters move from theory to application, demonstrating how to build LLM-powered chatbots and retrieval-augmented generation (RAG) systems, and how to incorporate ethics, safety, and bias awareness into prompt design. By the end, readers will have a reusable toolkit for crafting effective prompts across education, research, business, and technical use cases.

The availability of data and information across the internet grows exponentially with time. The web acts as a powerful and useful tool for users to access and retrieve relevant information. Search engines play a vital role in bringing relevant information to the users on a query basis. Web page recommendation systems play a vital role in identifying users' information requirements and make their browsing efficient. Numerous web page recommendation systems are built that chooses as well as suggests online pages that are appropriate with users‟ need of the hour. In current years, there is raising attention in using data mining techniques in the web content to construct web page recommendation systems. The primitive data mining techniques are not found to be efficient owing to their incapability to handle the pages that are created dynamically. PASO-WPR can carry out dependent upon incorporating semantic info with data mining techniques on the web usage data as well as clustering of pages dependent upon similarity in their semantics.