AI Fundamentals
More to discover ↗AI systems can sound certain when they are wrong, while a numerical confidence score may mean less than it appears to. Learn what common scores measure, why they need testing, and how to use uncertainty in everyday AI workflows.
Read article · 3 min↗An AI system that works well today may struggle when the work around it changes. Learn what model drift means, how to distinguish it from ordinary variation or a model update, and how to spot problems with practical checks.
Read article · 4 min↗A chatbot mainly responds to a conversation. An AI agent may also use tools, work through multiple steps and take delegated actions. The difference is less about a label than about what the system can access, change and do without asking first.
Read article · 4 min↗AI can give a clear, confident answer that is partly or entirely wrong. Learn why this happens, which claims deserve extra scrutiny, and a practical way to verify answers before relying on them.
Read article · 4 min↗A model that impresses in a demo may struggle with your everyday work. Learn how to build a representative test set, choose metrics that reflect real outcomes, and compare models without overlooking errors, cost or privacy.
Read article · 4 min↗A larger AI model is not automatically the better choice. Learn how model size affects speed, cost and capability, why the trade-offs vary by task, and how to test a small model before paying for more capacity.
Read article · 4 min↗Open-weight models let you run and adapt a model yourself, while hosted AI APIs provide access through someone else’s service. Neither option gives you complete control. This guide explains the practical differences in model behavior, data handling, updates, costs and reliability.
Read article · 4 min↗A useful prompt tells an AI system what you need, gives it the right context and makes the desired output easy to recognize. Learn a simple way to draft prompts, test one change at a time and check whether the result is accurate and useful.
Read article · 4 min↗Multimodal AI can work with more than one kind of input or output, such as written instructions, pictures and speech. Learn what that means in practice, how to structure a useful workflow, and where human review and privacy precautions still matter.
Read article · 4 min↗Retrieval-augmented generation, or RAG, lets an AI assistant consult selected documents before answering. Learn how retrieval works, why source quality matters, and how to check whether an answer is actually supported by the material it found.
Read article · 4 min↗An AI assistant can use a detail in your current chat, recall a saved preference or draw on patterns learned during training. Those abilities may look similar, but they work differently. Here is a practical guide to what each one does, where it can fail and what to check before sharing information.
Read article · 4 min↗Temperature and top-p influence how a language model chooses its next token. Learn what each setting does, why higher values do not automatically mean better ideas, and how to adjust them for practical tasks.
Read article · 4 min↗Generative AI produces new text, images and other content, while discriminative AI predicts labels or values from inputs. Learn how each approach works, where they overlap and how to choose one for a practical task.
Read article · 4 min↗Embeddings turn text and other content into numerical vectors that can help a system find related items even when they use different words. Learn what those numbers represent, how similarity search works, and how to build a useful search workflow without mistaking a close match for a correct answer.
Read article · 4 min↗A language model can learn broad patterns from large collections of data, then be trained to follow requests or adapted for a narrower task. Here is how pretraining, instruction tuning and fine-tuning differ—and why none of them guarantees a correct answer.
Read article · 4 min↗A large language model produces a response through a repeated cycle: read the available context, estimate which token could come next, select one, and continue. Understanding that cycle helps explain why prompts matter, why answers can vary, and why fluent text is not always reliable.
Read article · 4 min↗Attention helps a transformer use relevant parts of its input when processing each token. Learn what queries, keys, values, attention weights and multiple heads do—and what an attention map cannot tell you.
Read article · 4 min↗AI models process text as tokens, not as a neat sequence of words. Learn what tokens are, why the same sentence can be split in different ways, and how tokenization affects context limits, output length, and everyday prompting.
Read article · 4 min↗Training is how a model learns patterns from examples; inference is how it uses what it learned to respond to a new input. Understanding the difference helps you evaluate AI tools, design better workflows, and know when a model can—and cannot—learn from your use of it.
Read article · 4 min↗These three approaches differ mainly in what the training data tells a model to learn. Here’s how labels, hidden patterns and tasks created from the data itself shape useful AI workflows.
Read article · 4 min↗Neural networks turn inputs into outputs through a sequence of layers. Learn what weights and activations do, how training adjusts them, and why a useful prediction is not the same as understanding.
Read article · 4 min↗AI, machine learning and deep learning are related terms, but they are not interchangeable. Learn how they fit together, what each approach does and why the distinction matters when choosing a tool.
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