All Articles & Tutorials
Explore practical guides, step-by-step masterclasses, and foundational definitions across AI, SQL, and data systems — written in plain, human English.
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What data orchestration does, explained with one simple metaphor
Orchestration is kitchen management for data: schedules, dependencies, and retries. Learn the Airflow and dbt mental models so broken morning numbers point to a clear task, not a…
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.
How to quality-check work that Claude Cowork produces before you share it
Catch workslop before you share: verify numbers, sources, and claims even when the deck looks polished. Use a quality loop by artifact type and a paste-ready review card.
Should you build a dashboard or a single slide for your data?
Dashboards monitor change; single slides carry one decision. Use the one-idea-per-view rule and a declutter checklist so a busy ops page stops pretending to be a decision document.
Fine-tuning vs RAG: two ways to teach an AI chatbot about your company
Fine-tuning changes model behavior with examples. RAG retrieves documents into the prompt. Prefer RAG first for company knowledge; fine-tune when style or task format is the real gap.
