Module 2.1 · Topic 1

Foundational Prompt Craft

Bottom Line Up Front: Prompt clarity and specificity directly determine output quality. Explicit instruction, context, format, and reasoning requests produce reliable results. Output quality depends on precision. Learn…

1.1 Clarity and Specificity in Instructions

Specificity in scope, format, and constraints improves output quality.

Why it works:

Specifying format, length, and use case eliminates guesswork.

Sample prompt:

Create a 3-minute sales pitch for a new CRM platform targeting mid-market companies (50-500 employees). Lead with a specific business problem (e.g., customer data scattered across tools). Explain the solution in non-technical language. End with one ROI statistic. Keep sentences to 15 words or fewer. Use conversational, confident tone—no jargon.

What to expect:

A focused pitch addressing the exact problem, usable with minimal revision.

1.2 Providing Context and Background

Context shapes AI's response. Supplying background information like history, constraints, and past results invites situation-specific advice.

Why it works:

Context ensures responses account for your actual circumstances, not generic best practices.

Sample prompt:

Restructuring my support team: 12 people, 200+ daily inquiries, previous outsourcing failed (quality/satisfaction dropped 15%), homegrown ticketing system. Budget: no payroll increase, max $12K/month for tools. Goal: response time 6 hours→2 hours. Staffing and tool changes to prioritize in 6 months?

What to expect:

Situation-specific recommendations accounting for past failures and constraints.

1.3 Controlling Output Format and Structure

Specify format: bullet points, memo, table, or outline. Format choice determines usability and post-processing needs.

Why it works:

Specifying format saves post-processing and ensures immediate actionability.

Sample prompt:

Analyze 4-day work week for 50-person consulting firm. Deliver as table: Issue | Advantage | Risk (for us). Include 6+ issues. Assume clients expect Mon-Fri availability. Keep cells to 1-2 sentences.

What to expect:

A formatted table ready for leadership documents, eliminating post-processing.

1.4 Requesting Step-by-Step Reasoning

When the conclusion and considerations matter more than speed, ask AI to show its reasoning at each step. This transparency allows you to catch errors early and understand the logic supporting the recommendation.

Explicit reasoning: Ask AI to "explain your reasoning at each step" so you can verify logic before relying on conclusions.
Step-by-step analysis: Break problems into discrete steps so errors become visible and assumptions are challengeable.
Conclusion verification: Reasoning visibility lets you assess conclusions and understand the why the AI answered a particular way. This is especially critical for high-stakes decisions.