Mastering AI Chatbots: Content Generation, Editing & Optimization with ChatGPT, Gemini, Claude, & More

This lesson introduces the concept of using unstructured prompts to interact with ChatGPT effectively. Instead of predefined formats or structured input, learners will explore a flexible and iterative approach to refine prompts and generate desired outputs. This foundational knowledge enables users to harness ChatGPT’s potential in crafting and improving results step-by-step.

This lesson focuses on leveraging the editing tools within ChatGPT’s Canvas feature to refine and customize content. Learners will explore how to adjust content length, reading level, and overall polish without relying on prompts, ensuring seamless editing within the platform.

This lesson covers how to refine and enhance ChatGPT’s output using built-in editing features. Learners will explore various customization options, such as modifying text formatting, making direct edits, and using the WYSIWYG (What You See Is What You Get) editor to refine passages for clarity and emphasis.

This lesson highlights two advanced features of ChatGPT: audio interaction and GPT extensions. Learners will explore how to interact with ChatGPT using voice on mobile devices, listen to generated content, and extend functionality through specialized GPT models.

This lesson introduces how to use unstructured prompts in Meta AI, the chatbot extension of Facebook’s open-source technology. Learners will explore how Meta processes and structures content from an input prompt and compare its output to ChatGPT’s.

This lesson explores the built-in editing tools available in Meta AI, which allow users to modify and refine AI-generated content without additional costs. Learners will gain hands-on experience with Meta’s Remix feature, which provides variations of the original output, enabling content customization.

This lesson explores the different ways to output and manage edited content in Meta AI. Learners will discover how to copy, format, regenerate, and track multiple revisions within the platform, ensuring flexibility when working with AI-generated content.

This lesson highlights Meta AI’s experimental elements, which allow users to explore cutting-edge AI capabilities at no additional cost. Learners will discover how to access Meta’s AI Demos, create AI-generated audio stories, and test available experimental features.

This lesson introduces Perplexity AI, a chatbot with both free and pro versions. Learners will explore how to input unstructured prompts, analyze Perplexity’s step-by-step content generation, and compare word count accuracy with the requested input.

This lesson explores the built-in rewriting and expansion tools in Perplexity AI. Learners will discover how to regenerate content, choose AI models for rewriting, and leverage related questions and topics to expand their content.

This lesson explores how to output, manage, and refine Perplexity AI’s generated content. Learners will discover how to copy and paste content, utilize hyperlinked sources, and control document regeneration by modifying sources.

This lesson highlights special features in Perplexity AI, including image search and generation, document uploads, and focused queries for specific research needs. Learners will explore how to optimize Perplexity AI as a research tool.

This lesson introduces Google Gemini, a chatbot by Google, and demonstrates how to use unstructured prompts to generate responses. Learners will explore Gemini’s free and paid versions, analyze content output accuracy, and learn how to extract and format responses.

This lesson explores Google Gemini’s built-in editing tools, which allow users to refine AI-generated content without manually inputting new prompts. Learners will discover how to use draft variations, content regeneration, and modification tools to enhance their responses.

This lesson explores the various ways to export and share AI-generated content in Google Gemini. Learners will discover how to export to Google Docs, draft emails in Gmail, and create public shareable links using Gemini’s built-in tools.

This lesson highlights special features in Google Gemini, including audio interaction for both listening to AI-generated responses and submitting voice queries using a microphone.

This lesson introduces Claude AI, a chatbot by Anthropic, and demonstrates how to use unstructured prompts to generate responses. Learners will explore Claude’s different models, analyze its split-screen artifacts feature, and learn how to extract content in various formats.

This lesson explores Claude AI’s approach to content editing, focusing on how users must rely on prompts for revisions since there are no direct built-in editing tools. Learners will also discover how to use Claude Artifacts and revision tracking to manage content updates.

This lesson explores the different ways to export and share AI-generated content in Claude AI. Learners will discover how to copy, download, and publish content, as well as how markdown files work for document conversion.

This lesson explores Claude AI’s Artifacts feature, which allows users to generate interactive content, code snippets, website designs, infographics, and mind maps. Learners will discover how to use Claude Artifacts for more than just text generation.

This final lesson covers how to analyze AI-generated documents across multiple chatbots and synthesize insights using NotebookLM. Learners will discover how to compare chatbot outputs and use AI-powered tools for deeper meta-analysis.