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AI Basics
Plain-English explainers for core AI concepts, model terms, prompting patterns, and the practical skills people need to use AI well.
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Showing articles 19-36 of 37 in AI Basics.
What Is a Vector Database? Why AI Apps Store Meaning as Searchable Numbers
A vector database stores embeddings as searchable numerical representations so AI apps can retrieve related documents, products, images, or knowledge base chunks by similarity rather than exact keywords.
What Is Temperature in AI? How Randomness Changes ChatGPT Responses
Temperature in AI controls how much randomness a model uses when generating responses. It affects why ChatGPT outputs vary, how creative or consistent answers feel, and how much risk a workflow accepts.
What Is Prompt Engineering? Beginner Examples, Best Practices and Common Mistakes
Prompt engineering is the practical skill of writing clearer AI instructions. This beginner explainer defines the concept, shows examples, explains best practices, and flags common mistakes that lead to weak or risky AI responses.
What Is MCP in AI? The Model Context Protocol Explained for Beginners
A beginner-friendly explainer of MCP as the open standard that connects AI assistants to tools, files, data sources, systems and workflows, with practical examples and security caveats.
What Is a Token in AI? A Beginner's Guide to AI Tokens, Costs and Context Windows
AI tokens are the small units of text or data that models process. They matter because token counts shape AI pricing, context windows, memory-like working context, output length, latency, and usage limits.
What Is Grounding in AI? How to Make AI Answers Use Trusted Sources
A beginner-friendly explainer of AI grounding, source-backed answers, RAG, citations, and the practical habits that help reduce unsupported AI claims.
What Is Tool Calling in AI? How Models Use APIs, Apps and External Data
A clear beginner-friendly explainer of tool calling in AI, including how models request functions, APIs, apps and external data, plus practical examples and safety guidance.
What Is AI Bias? How Training Data Can Create Unfair AI Outcomes
A plain-English explainer of AI bias, how training data can create unfair outcomes, and why biased AI systems matter for business, search, hiring, finance, and public services.
AI Chatbot vs AI Agent: What's the Difference for Beginners?
A beginner-friendly comparison of AI chatbots and AI agents, including reactive chat, goal-oriented planning, tool use, autonomy, examples, risks, and when to choose each one.
Why Does AI Hallucinate? What AI Hallucinations Are and How to Reduce Them
A practical explainer of AI hallucinations, why generative AI can produce false or unsupported answers, and how to reduce risk with grounding, citations, verification, and human review.
What Is a Foundation Model? Why Modern AI Models Can Be Reused for Many Tasks
A plain-English explainer of foundation models as large reusable AI base models, covering how they work, how they are adapted, where they appear in real products, and why their strengths and weaknesses travel downstream.
What Are AI Model Parameters? The Difference Between Model Size, Settings and Controls
AI model parameters are learned internal values, often weights and biases. This explainer separates model size from user settings like temperature, top_p and max output tokens.
What Is Generative AI? A Simple Guide to AI That Creates Text, Images, Code and Video
Generative AI is artificial intelligence that creates new content, including text, images, audio, video and code. It differs from traditional AI because traditional AI usually predicts, classifies, detects, ranks or recommends, while generative AI produces new outputs from learned patterns and prompts.
What Is Responsible AI? A Beginner's Guide to Safe, Fair and Transparent AI
A beginner-friendly explainer of responsible AI principles, governance, transparency, bias, fairness, and the practical controls teams use to manage AI risk.
What Is a Large Language Model? How LLMs Power ChatGPT, Claude and Gemini
A plain-English explainer of large language models, including training data, token prediction, how LLM-powered products work, common use cases, benefits, limitations and practical review habits.
What Is a Neural Network in AI? A Simple Explanation for Beginners
A beginner-friendly explainer of artificial neural networks as layered pattern-learning models, including how neurons, layers, weights, biases, training, examples, benefits, and limitations fit together.
What Is Deep Learning? How Neural Networks Help AI Understand Complex Data
A beginner-friendly explainer of deep learning as machine learning with layered neural networks, including how deep learning works, why layers matter for complex data, real-world examples, benefits, limits and related AI terms.
What Is Artificial Intelligence? A Beginner's Guide to AI in Everyday Life
A complete beginner guide to artificial intelligence, with a plain-English definition, everyday examples, common use cases, key terms, benefits, limitations, and FAQs.