AI for Consulting Case Prep: Practice and Review Your Reasoning

Use AI for consulting case prep as a practice partner: have it present one question at a time, challenge your reasoning, and review your approach after you finish. Check its calculations and coaching against the case evidence and official firm resources. The goal is to improve how you solve unfamiliar problems, rather than collect polished model answers.

Tools like fenbs keep practice prompts and recurring lessons between sessions, including when you change assistants. Your next mock interview can target the weakness you actually observed. This guide focuses on interview practice; AI tools for consulting firms cover a different stage of work.

How do you set up a useful AI practice session?

1. Choose one skill to practice, such as clarifying the objective, interpreting a chart, or giving a concise recommendation.
2. Start with an official public sample case or ask for a clearly fictional case. Keep the source answer hidden until your own attempt is complete.
3. Tell the assistant to act as the interviewer, ask one question at a time, and withhold solutions unless you request a hint.
4. Solve aloud or write your reasoning before sending each response. Check the facts and math yourself, then compare the feedback with the actual case.
5. Choose one lesson and schedule a new attempt that tests it without reusing the same answer.

Check your target employer’s instructions before choosing a format. McKinsey’s interview resources↗ (opens in a new tab) describe its problem-solving interviews and provide sample cases. Treat those as the source for that firm’s expectations; a model’s confident description of a hiring process is not enough.

Keep practice separate from an actual assessment. Follow the recruiter’s rules about outside assistance, and do not send confidential interview materials to an AI tool. You can create new practice scenarios without copying questions you were asked to keep private.

What should you ask the AI interviewer to do?

Give it a narrow role and clear boundaries. Otherwise it may solve its own question, praise a weak answer, or introduce data that changes the problem halfway through. Ask it to preserve a stable set of facts and identify any extra assumptions explicitly.

Act as a practice interviewer for a fictional consulting case.
Focus on identifying the objective and choosing useful analysis.
Give me the opening situation, then wait for my response.
Ask one question at a time. Do not show the solution early.
Keep case facts fixed; label added assumptions.
If I request a hint, give the smallest useful hint.
After my recommendation, review specific reasoning steps.
Separate factual errors, math errors, and communication feedback.
Do not claim to predict an employer’s hiring decision.

Set a hint rule you can measure. For instance, try to explain your next step before asking for help, and record when you needed a prompt. A smooth answer produced after several hints is a different result from an independent solution, even if both end with the same recommendation.

Store the prompt with your chosen variations in fenbs Skills and prompts. Keep the training objective visible so switching models does not quietly change what you are practicing.

Which drills work well between full cases?

Use short drills when you can name the weak step. Repeat the reasoning pattern with different numbers or circumstances so you learn the method. Full cases then test whether you can recognize when that method is useful without someone naming it for you.

For a fictional math drill, suppose capacity is 120 service appointments each day and utilization is 75 percent. That implies 90 appointments per day, before considering cancellations or other changes. Explain both the calculation and why capacity matters to the client’s question; correct arithmetic alone does not select a strategy.

How do you check whether the AI feedback is sound?

Ask the assistant to point to your exact answer and the case fact behind each criticism. Recalculate numeric claims with pen and paper or a calculator. When it says your structure missed a factor, ask how that factor could change the decision.

Use Bain’s case interview preparation resources↗ (opens in a new tab) as another primary reference. Its guidance emphasizes clarifying the objective, explaining reasoning, and adapting to the conversation. That supports reviewing your approach, rather than assuming one memorized structure fits every case.

Treat a numeric AI score as a coaching opinion, not a prediction of hiring success. Look for observable evidence: you skipped a necessary question, used inconsistent units, or gave a recommendation unsupported by the analysis. If a critique cannot be connected to your attempt, set it aside until you can test it.

What should you save after each attempt?

Save the case source or fictional setup, the skill practiced, the help you received, and one specific correction. Keep a short example of your reasoning so you can tell whether the same problem recurs. Avoid storing only a model’s flattering summary of the session.

A useful lesson reads: “I compared daily capacity with monthly demand. Next time I will write the period beside each number before calculating.” Turn that lesson into a new drill and check whether you apply it without a reminder. An AI brain is useful here because it keeps the learning record outside the temporary conversation.

How to do this with fenbs

1. Create a free account and add a consulting interview preparation project.
2. Save your interviewer prompt in Skills and prompts. Write your practice rules, such as withholding hints until requested, in Decisions and rules.
3. Add tasks for the skills you want to practice, with the drill and acceptance check in each plan. Record observed mistakes as Lessons learned after each attempt.
4. Connect a supported assistant from Integrations, ask it to read the project context, and use that context to guide practice. Save progress with the next skill to test before stopping.

fenbs does not run a case interview or grade candidates. You need an AI app or a human practice partner for the conversation. Use fenbs when keeping lessons between sessions helps; a simple personal note can be enough for an occasional drill.

When should you practice with another person?

Include human mock interviews so you can test whether another person follows your reasoning and finds your answer convincing. Ask them where they lost the thread, which assumptions needed explanation, and whether you responded to their cues. Bring those observations back into your practice record.

Finish your next session with one independent check: can you solve a new version without hints and explain the result plainly? For related workflows, see using AI at work and the AI at work hub. Keep improvement tied to what you can now do yourself.