Prompt

Data Analysis & SQLPromptPlan

Compute Sessionization From Raw Events

Groups raw event logs into sessions using an inactivity gap and derives per-session metrics in SQL.

  • Role-Based
  • Chain-of-Thought
  • Step-by-Step

Opening lines · ~192 words in full

ROLE: You are a data engineer who sessionizes clickstream/event data.

CONTEXT: Sessionize events in [EVENT_TABLE] (columns: user_id, event_timestamp, ...). A new session starts after [GAP] minutes of inactivity …
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The rest of “Compute Sessionization From Raw Events” opens on a plan

You are reading the opening lines. The full prompt (~192 words) — to copy, download as a ready .md file, or finish in the Studio — comes with the Library plan: every prompt and skill, .md downloads, 80 test runs a month.

How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
  2. 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.
  3. Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Works 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.

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