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Explain retrieval-augmented generation in plain language, including retrieval and generation steps, how RAG lets AI answer from web pages documents PDFs company knowledge bases and current sources, where users see it in ChatGPT Gemini Copilot NotebookLM and support bots, how citations reduce hallucinations, and why bad retrieval still causes bad answers.

Explain prompt engineering as the skill of writing testing and refining AI instructions, when it matters for repeated business prompts more than one-off chats, techniques such as role assignment few-shot examples step-by-step reasoning output constraints and iterative testing, context engineering, and how beginners can practice on recurring tasks.

Explain Perplexity AI as an answer engine with cited sources, what the free plan generally includes, account history basic searches daily advanced-model limits file uploads and image limits, how Pro trials usually work with payment methods automatic renewal and cancellation, mobile apps voice mode and how Perplexity differs from traditional search.

Explain open source and open weight AI models, including downloading running self-hosting modifying and fine-tuning models, examples such as Llama Mistral DeepSeek and Stable Diffusion, compare privacy cost capability ease of use and customization against closed chatbots, clarify open weights versus open data, and warn that open does not automatically mean safer or more truthful.

Explain Midjourney for beginners, including text prompts image grids upscaling variations Discord and web access subscriptions limited trials commercial use on paid plans privacy and Stealth Mode, where generated images are saved, how to create a first image, and how Midjourney compares with ChatGPT image generation Gemini and Stable Diffusion.

Explain multimodal AI as systems that handle text images audio and video, compare unimodal and multimodal assistants, describe practical uses such as photo questions voice mode live camera screen sharing image generation and document chart analysis, warn about mistakes with complex images noisy audio and sensitive topics, and note feature limits by plan.

Explain generative AI as AI that creates new text images audio video and code instead of only labeling or predicting, compare it with older analytical AI, describe text image audio voice and video branches, explain why outputs vary, why training data raises copyright concerns, and why hallucinations must be verified.

Explain AI fine-tuning as extra focused training on an existing model, compare it with prompts custom instructions system prompts and RAG, explain why it is useful for style tone format and repetitive specialized tasks, why it is usually developer-facing, and why RAG is better for changing facts.

Explain machine learning in plain language, including how it learns from examples instead of fixed rules, how it relates to AI and generative AI, examples like spam filters recommendations fraud detection autocorrect and chatbots, why data quality and bias matter, and the difference between training and inference.

Explain Microsoft Copilot Pages as a persistent editable shared canvas for Copilot responses, when to use Pages instead of chat, how to create edit revise share or export a Page, how it differs from Word, and which features may depend on Microsoft 365 Copilot work or school plans.