Every explainer on the site, grouped by topic. Written to stay useful as products change: we explain how to judge a tool rather than ranking this month's leader.
A model does not read your database the way you do. Understanding what it actually sees explains most of its confident, wrong joins.
Vectors are not a replacement for feature engineering. Knowing which problems they fit saves you from an expensive detour.
Three concepts explain most of what surprises analysts about LLM behavior, cost, and reliability. Here is what each one actually controls.
A practical architecture for letting analysts ask questions in English while the model never sees your raw schema, PII, or ungoverned joins.
The failure isn't bad SQL syntax — it's confident, wrong numbers that look exactly like right ones. Here's where they come from and how to catch them.
Three frontier assistants, the same messy exploratory workflow, and honest trade-offs on where each one earns a place in your stack.
A repeatable workflow for converting coefficient tables into a memo a VP will actually read, with the LLM doing structure and you keeping the claims honest.
A line between the documentation an LLM should generate wholesale and the sentences only the person who built the model can honestly write.
How to review an AI-drafted executive summary so it holds up when a skeptical leader pushes on every number and claim in the room.
A vision model won't tell you if your numbers are right, but it will catch the layout problems you stopped seeing three revisions ago.
Vega-Lite's declarative JSON grammar is one of the few chart formats an LLM can produce reliably — if you give it the schema and check three specific things.
Language models are trained on what looks pretty, not on perceptual math, so their palettes fail colorblind and contrast checks in predictable ways you can correct.
How to catch renamed and reordered upstream columns automatically without letting a language model silently corrupt your schema.
Generated pandas aggregations often run clean and return the wrong number; here is how to prompt and verify so the logic is actually right.
A read-only triage agent that classifies why a dbt or Great Expectations test failed and routes it, without ever touching your data.
Token prices are the smallest line item. Here is a cost model that accounts for retries, context bloat, human review, and the warehouse bill underneath it.
Finance is not against AI. They are against unmeasured spend. Here is how to frame an analytics AI budget in terms they will approve.
The rungs are not disappearing, but the work on each one is changing. Here is what compounds in value and what is quietly commoditizing.