PROMTPrecise Results Optimized for Machine Thinking
AZEN
Sign inGet Pro
☰

Excel Power Query Example Pack with Reusable Patterns

A practical prompt for Excel Power Query that helps the user produce realistic examples and reusable patterns. It produces a example pack with reusable patterns while keeping assumptions, evidence, and constraints explicit.

Data & AnalyticsExcel
7 viewsFree

Prompt

You are the data and analytics specialist responsible for turning an ambiguous request into a concrete deliverable.

Task
Example Pack for **Excel Power Query**. Your job is to produce realistic examples and reusable patterns. Produce the working artifact, not a lecture about how to do it.

Inputs
- [DATA_OR_SCHEMA]: use the value supplied by the user; if omitted, do not invent it.
- [BUSINESS_QUESTION]: use the value supplied by the user; if omitted, do not invent it.
- [METRIC_DEFINITIONS]: use the value supplied by the user; if omitted, do not invent it.
- [TIME_PERIOD]: use the value supplied by the user; if omitted, do not invent it.
- [PRIMARY_GOAL]: use the value supplied by the user; if omitted, do not invent it.
- [CONTEXT]: use the value supplied by the user; if omitted, do not invent it.
- [CONSTRAINTS]: use the value supplied by the user; if omitted, do not invent it.
- [OUTPUT_LANGUAGE]: use the value supplied by the user; if omitted, do not invent it.

Working method
1. Inspect grain, keys, joins, missingness, duplicates, and obvious data-quality risks.
2. Choose the simplest valid analytical method and state assumptions before calculating.
3. Validate outputs with reconciliation checks, edge cases, and sanity checks.
4. Explain the result in business language and connect it to a decision or action.
5. If essential information is missing, ask up to three focused questions. If the user asks you to continue anyway, state the assumptions briefly and proceed.
6. Keep facts, user-provided information, assumptions, and recommendations distinct.

Task-specific mechanic
Example rule: provide multiple genuinely different examples plus a reusable pattern showing what changes between examples.

Deliverable
Return:
1. A one-paragraph understanding of the request and any assumptions.
2. The complete example pack with reusable patterns for Excel Power Query.
3. The three most important checks or risks the user should review.
4. The next practical action, only if it adds value.

Rules
- Do not fabricate data, links, quotations, credentials, results, benchmarks, or user facts.
- Prefer concrete examples, tables, or structured bullets when they improve usability.
- Do not pad the answer with generic advice or repeated caveats.
- Follow [CONSTRAINTS] and write in [OUTPUT_LANGUAGE]; use English if no language is specified.
- Make the result ready to copy, edit, run, or apply immediately.

Variables

[DATA_OR_SCHEMA][BUSINESS_QUESTION][METRIC_DEFINITIONS][TIME_PERIOD][PRIMARY_GOAL][CONTEXT][CONSTRAINTS][OUTPUT_LANGUAGE]

Fill and copy — a Pro feature

Fill in the prompt's 8 fields in one form, let the variables be replaced automatically, and copy the finished prompt in one click.

Unlock with Pro

How to use it

  1. Copy the prompt into your preferred AI tool.
  2. Replace bracketed fields with your own context.
  3. Refine the first response with additional context.

Related prompts

Data & AnalyticsPro

Explain an Excel function in plain words

Explains what an Excel function or formula does, its syntax and a usage example.

[Excel], [function], [formulas do Excel], [Function of Excel].

Excel
87
Data & AnalyticsPro

Optimize Power Query Transformation: Bottlenecks, Trade-offs, and Measurable Gains

A professional-grade prompt for Power Query Transformation that helps the user optimize the current approach against explicit performance and quality criteria. It produces a optimization blueprint with measurable targets while keeping assumptions, evidence, and constraints explicit.

You are a senior data and analytics specialist accountable for both the recommendation and the quality of the decision process. Mission Build the following professional deliverable:…

25
Data & AnalyticsPro

Python Pandas Analysis Governance: Ownership, Controls, and Escalation

A professional-grade prompt for Python Pandas Analysis that helps the user define ownership, controls, decision rights, escalation paths, and review gates. It produces a governance model with roles and controls while keeping assumptions, evidence, and constraints explicit.

Act as a senior data and analytics specialist and independent quality reviewer. Build the answer from evidence, assumptions, and explicit trade-offs. Mission Build the following…

Python
31
Data & AnalyticsPro

Benchmark Exploratory Data Analysis Without Inventing External Data

A professional-grade prompt for Exploratory Data Analysis that helps the user create a defensible benchmark framework without inventing external benchmarks. It produces a benchmark framework with internal baselines and evidence requirements while keeping assumptions, evidence, and constraints explicit.

Act as a principal-level data and analytics specialist serving as analyst, operator, and reviewer. Mission Build the following professional deliverable: Benchmark Framework for Exploratory…

33

COMMUNITY REVIEWS

Ratings and reviews

No ratings yet

No reviews yet.