Curriculum

21 Chapters · 21 Lessons

  1. 1

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  2. 2

    Chapter 1: Discover the GenAI Revolution from Hype to Real Impact

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      Discover the GenAI Revolution from Hype to Real Impact

  3. 3

    Chapter 2: How Large Language Models Understand and Generate Text

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      How Large Language Models Understand and Generate Text

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    Chapter 3: Build Your AI Vocabulary with Tokens, Prompts, and Embeddings

    1. (Included in full purchase)

      Build Your AI Vocabulary with Tokens, Prompts, and Embeddings

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    Chapter 4: Choose Your AI Partner by Exploring GPT, Claude, and Gemini

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      Choose Your AI Partner by Exploring GPT, Claude, and Gemini

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    Chapter 5: Create AI Solutions without Code Using No-Code Platforms

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      Create AI Solutions without Code Using No-Code Platforms

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    Chapter 6: Master Prompt Engineering to Make AI Work for You

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      Master Prompt Engineering to Make AI Work for You

  8. 8

    Chapter 7: Build Your First AI App by Turning Ideas into Code

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      Build Your First AI App by Turning Ideas into Code

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    Chapter 8: Create Smart Workflows by Chaining AI Actions Together

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      Create Smart Workflows by Chaining AI Actions Together

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    Chapter 9: Design Beautiful AI Apps with Professional User Interfaces

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      Design Beautiful AI Apps with Professional User Interfaces

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    Chapter 10: Teach AI to Understand Meaning with Semantic Search

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      Teach AI to Understand Meaning with Semantic Search

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    Chapter 11: Build Knowledge Bases with Vector Databases

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      Build Knowledge Bases with Vector Databases

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    Chapter 12: Create AI with Memory Using RAG Systems that Remember

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      Create AI with Memory Using RAG Systems that Remember

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    Chapter 13: Build Reasoning Agents with Design Thinking

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      Build Reasoning Agents with Design Thinking

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    Chapter 15: Deploy AI Privately on Your Own Hardware

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      Deploy AI Privately on Your Own Hardware

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    Chapter 16: Launch AI Apps on Cloud

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      Launch AI Apps on Cloud

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    Chapter 17: Accelerate Development with AI-Powered Coding Assistants

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      Accelerate Development with AI-Powered Coding Assistants

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    Chapter 18: Build Responsible AI with Ethics and Safety Best Practices

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      Build Responsible AI with Ethics and Safety Best Practices

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    Chapter 19: Building a Sustainable Career in Generative AI

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      Building a Sustainable Career in Generative AI

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    Chapter 20: Capstone Projects Using Generative AI

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      Capstone Projects Using Generative AI

  21. 21

    Index

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      Index

About the Course

From Natural Language to Intelligent Systems at Production Scale Generative AI is reshaping how software gets built, how businesses operate, and how professionals deliver value — and the ability to move from a natural language idea to a working AI application is the defining skill of the next decade. Prompting Generative AI for Intelligent Applications shows you how to translate plain language intent directly into LLM applications, RAG pipelines, and intelligent agents using AI, accelerating every stage of development from first prompt to production deployment. Rather than teaching AI theory from scratch, this book puts prompt-driven execution at the centre. You use prompt engineering techniques to build chatbots, chain AI actions, design knowledge bases with vector databases, and orchestrate multi-agent systems, then deploy them across cloud and private infrastructure with confidence. Every chapter is oriented around getting working AI output faster, not theory. By the end of the book, you will use prompts as a core part of your AI development workflow, building and deploying intelligent applications with greater speed, clarity, and production confidence than traditional development approaches allow.

About the Author

Jit Sinha is an accomplished Enterprise Architect with 15 years of experience, specializing in cloud platforms and cybersecurity. He designs scalable solutions across banking, telecom, and healthcare. A Splunk expert and AI enthusiast, he also shares knowledge as a speaker and instructor, simplifying complex technologies.