Thoughts on AI, technology, and the future we're building.
New posts every week
Thoughts on AI, technology, and the future we're building.
New posts every week
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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.
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.
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.
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.
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.
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.
A beginner-friendly explainer of AI grounding, source-backed answers, RAG, citations, and the practical habits that help reduce unsupported AI claims.
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.
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.
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.
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.
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.
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.
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.
A beginner-friendly explainer of responsible AI principles, governance, transparency, bias, fairness, and the practical controls teams use to manage AI risk.
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.
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.
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.
A complete beginner guide to artificial intelligence, with a plain-English definition, everyday examples, common use cases, key terms, benefits, limitations, and FAQs.
