40+ ChatGPT & Claude prompts for Consultants & Coaches

ROLE: You are a seasoned management consultant preparing for a discovery phase with a new client.

CONTEXT: The client is [CLIENT TYPE/INDUSTRY]. The presenting problem they've described: [STATED PROBLEM]. My hypothesis about what might really be going on: [INITIAL HYPOTHESES]. The stakeholders I can interview: [ROLES]. My objective for discovery: [WHAT I NEED TO LEARN BEFORE SCOPING].

TASK:
1. Reframe the presenting problem into the deeper questions worth investigating — the stated problem is rarely the real one.
2. Build a structured question bank organized by theme: current state & process, pain points & impact, prior attempts & why they failed, success criteria, constraints & politics, and data availability.
3. For each theme, include open diagnostic questions, a few targeted probes, and one 'question behind the question' that gets at unspoken issues.
4. Tailor a short subset of questions per stakeholder role (an exec hears different questions than a front-line operator).
5. Flag the questions most likely to reveal whether my initial hypotheses are right or wrong.
6. Note the data/documents to request alongside the interviews.

OUTPUT FORMAT:
- Reframed core questions
- Question bank by theme (with probes)
- Role-specific question subsets
- Hypothesis-testing questions (which hypothesis each tests)
- Data/document request list

CONSTRAINTS: Favor open, non-leading questions — don't smuggle in the answer. Avoid yes/no questions where a story is more revealing. Keep it usable in real interviews (prioritized, not 100 questions). Respect that some questions are politically sensitive — note how to ask them carefully. Don't propose solutions yet; this is purely diagnostic.

ROLE: You are an operations strategist who coaches teams to write OKRs that are ambitious, measurable, and not vanity metrics.

CONTEXT: My team is [TEAM/FUNCTION]. The strategic priority for this quarter is [PRIORITY]. Context on our current baseline metrics: [CURRENT NUMBERS]. Constraints: [HEADCOUNT, BUDGET, DEPENDENCIES].

TASK:
1. Draft 2-3 Objectives that are qualitative, inspirational, and clearly tied to the priority.
2. For each Objective, write 3-4 Key Results that are quantitative, outcome-based (not task lists), and have a baseline and a target.
3. Stress-test each Key Result against four tests: Is it measurable? Is it an outcome not an output? Is it ambitious yet achievable? Could we game it? Flag and fix any that fail.
4. Identify the leading indicators we should track weekly to know if we're on pace.
5. List the top 3 dependencies or risks that could derail these OKRs.

OUTPUT FORMAT:
- Objective blocks, each with its Key Results (KR | Baseline | Target | Owner)
- Stress-test notes per KR
- Weekly leading indicators
- Dependencies & risks

CONSTRAINTS: No KR may be a to-do item ('launch X') unless it has a measurable success threshold. Avoid more than 3 objectives — focus over coverage. Every target must reference the baseline I gave you or explicitly note the assumed baseline. Push back if a proposed objective is actually just business-as-usual.

ROLE: You are a business-intelligence and operations consultant who designs KPI systems that drive behavior, not vanity dashboards.

CONTEXT: The team/business is [NAME, FUNCTION]. The overarching goal we're trying to move is [NORTH-STAR / PRIMARY OUTCOME]. The data we can realistically capture: [AVAILABLE METRICS / SYSTEMS]. The audience for the dashboard: [WHO USES IT AND HOW OFTEN].

TASK:
1. Define one north-star metric that best represents value created, and explain why it's the right top-line.
2. Build a metric tree: decompose the north-star into 3-5 lagging outcome metrics, then into the leading/input metrics that teams can actually influence weekly.
3. For each KPI, specify: definition (no ambiguity), formula, target/benchmark, owner, and review cadence.
4. Distinguish leading from lagging indicators and explain how the leading ones predict the lagging ones.
5. Identify the 'guardrail' metrics that must not degrade while we chase the goal (e.g., quality, cost, burnout).
6. Recommend the minimal dashboard layout: what's on the executive view vs. the operational view.

OUTPUT FORMAT:
- North-star metric + rationale
- Metric tree (visualized as nested bullets)
- KPI definition table (KPI | Formula | Target | Owner | Cadence | Leading/Lagging)
- Guardrail metrics
- Dashboard layout recommendation

CONSTRAINTS: Fewer metrics, better — resist a wall of numbers. Every KPI must be actionable by someone; cut metrics nobody owns. Define formulas precisely so two people compute them identically. Flag any metric that is easily gamed and how to counter it. Note where the needed data isn't yet captured.