6 articles
Browse guides
A clear breakdown of AI versus machine learning: definitions from OpenAI and Google, a side-by-side comparison table, rule-based versus learning-based workflows, real-world examples, a project framing checklist, and the misconceptions to drop.
A working guide to AI context windows: how the token limit is shared between input and output, how context differs from memory, small versus large window tradeoffs, an eight-step optimization checklist, and a repeatable workflow for tasks that do not fit.
A plain-language guide to foundation models: how they are pre-trained and adapted, how they compare with task-specific models, real applications, benefits and limits, ethical risks, and a five-question evaluation framework.
A practical guide to tokens in AI: how tokenizers split text into words, subwords, and characters, how tokens relate to context windows, a token-budgeting workflow for cost planning, and habits for managing long-context requests.
A practical guide to multimodal AI: how encode, align, fuse, and generate work in practice, real applications from search to maintenance, the benefits and failure modes, and an eight-step adoption checklist.
A practical explainer on RAG: the six stages of a retrieval pipeline, how it compares with prompt-only generation and keyword search, common applications, limitations, and a framework for deciding whether to build one.