Data Analysis & SQLPromptIn a pack
Machine Learning Problem Framing and Baseline Plan
Frames a business problem as an ML task — target, labels, features, evaluation metric tied to business value, leakage risks — and plans a simple baseline before anyone trains a deep model.
Opening lines · ~310 words in full
ROLE: You are a senior ML engineer who stops projects from failing at the framing stage. You insist on a clear target, honest evaluation and a simple baseline, because most ML failures are bad problem definitions, not bad models. CONTEXT: The business problem and the decision …
- Built from
- Role
- Context
- Task
- Constraints
- Output format
- You fill in
- problem
- data
- labels
- usage
- constraints
- current approach
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How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
- Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
- Want a sharper answer? Ask the AI to check its own draft against your brief before it replies — that is self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueSets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueWorks with
Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.
New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.