This path is for analytics people who will use AI on a real file, not a demo. Parts cover what it can and cannot do, tokens and cost as a feel, prompts for data work, checking an answer, retrieval in plain English, agents, what you should not paste, documentation, and a checklist you can reuse. Start with the first part on this page.
- 1 What AI can and cannot do for analysis AI is a sharp intern for analysis, not a signed-off analyst. Part 1 of Practical AI for analytics people maps assist versus replace, where liability for numbers still sits with you, and when to refuse a generated answer.
- 2 How tokens and context windows decide what an AI remembers and what it costs Treat context windows like a suitcase: pack role, schema crumbs, and the question. Chunk instead of cramming. Tokens are not free, and middle content gets lost first.
- 3 How to write AI prompts for data analysis work Build data prompts with role, spec, output shape, and refuse rules. Make grain, filters, and calendar explicit so the model cannot hide a wrong query behind fluent SQL.
- 4 How to check an AI tool’s answers yourself before you trust it at work Build human-sized evals: spot checks, golden questions, and a regression set with clear pass criteria. Score analytics outputs before fluent wrong SQL or prose becomes process.
- 5 What is RAG? How AI answers questions from your own documents RAG retrieves company passages, stuffs them into the prompt, then generates an answer. Use it for definitions and runbooks with citations. Do not treat grounding as a warranty for board numbers.
- 6 How to set limits on an AI agent before it touches company data Separate chatbot, script, and agent. A harness is the wrapper of tools and guardrails. Put a human in the loop before agents touch revenue or customer data.
- 7 Privacy when pasting data into chat tools Treat every paste as a processing decision. Use a risk ladder and synthetic samples. Consumer chat is not your approved enterprise workspace.
- 8 How to use AI to write data documentation and data dictionaries Use AI to draft dictionary entries, then verify against a signed source before publish. Draft, verify, publish is the only flow that stays honest.
- 9 How to build a personal checklist for checking AI-generated work Close Practical AI with one checklist that travels across SQL, charts, Python, and email. Verify every draft against a signed source before it leaves your desk.