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Analysis Prompting Style: Declaring Role and Knowledge Domain

Overview

For effective and reliable AI-driven analysis, prompts should begin by explicitly declaring the intended role and the relevant knowledge domain. This establishes context, sets expectations, and guides the model’s reasoning and language style.

Why Declare Role and Domain?

  • Role Declaration: Instructs the model to adopt a specific perspective (e.g., software engineer, security auditor, data scientist).
  • Domain Declaration: Focuses the model’s attention on the relevant field or subject matter (e.g., Python projects, web security, machine learning).

Explicitly stating both helps: - Reduce ambiguity - Improve relevance and accuracy - Align output with user intent

Example Structure

You are a(n) [role] with expertise in [domain]. Your task is to...

Example Prompts

  • You are an expert software project analyst. Your task is to analyze the provided project files and identify the core technologies used in the project.
  • You are a security auditor specializing in web applications. Review the configuration files for potential vulnerabilities.
  • You are a data scientist with experience in time series analysis. Examine the dataset and summarize key trends.

Extending with Analysis Actions

After declaring the role and domain, extend the prompt with clear, actionable analysis instructions.

Guidance:

  • Clearly state the analysis objective (e.g., "identify core technologies", "summarize vulnerabilities", "extract key metrics").
  • Specify the expected output format (e.g., bullet points, summary, table).
  • Avoid ambiguity—list exactly what should be included or excluded.
  • Use the condition-before-action (CBA) structure for any prerequisites or constraints.

Best Practices

  • Always start with a clear role and domain statement.
  • Extend with explicit analysis actions and output requirements.
  • Use precise, unambiguous language.
  • Follow with condition-before-action (CBA) structure for instructions (see Prompt Design Style).
  • Avoid vague roles (e.g., "expert"). Specify the field or context when possible.

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