THE AI PROMPTS From Hire Like Your Wallet Depends On It by Lenny Saucier learndevelopmanage.com/downloads/tsquare ============================================================ THE QUICK SCAN PROMPT Chapter 1, Frankenstein Research Finds your local competitors and builds the review search list for your Frankenstein research. Before you paste: Change the position in the first line ([asst manager]) to the job you are hiring for before you hit send. ------------------------------------------------------------ I’m hiring for a [asst manager] and I need to identify local competitors for hiring research. Before you do anything, I need you to ask me a few questions one at a time. Wait for each answer before moving on. Question 1: What type of business or industry am I in? (Examples: quick lube, dental practice, hair salon, auto repair, coffee shop, daycare, plumbing company, restaurant) Question 2: What’s my zip code? Question 3: How wide a radius should I search? (Suggestions: 5 miles for urban areas, 10–15 miles for suburban, 20–30 miles for rural. We can expand later if needed.) Once I’ve answered all three, do the following: 1. Identify up to 10 competitors in that area matching the business type. 2. For each one, list: - Business name - General location (street name or neighborhood) - Approximate size if known (single location, small chain, large chain) - How long they’ve been in business if known - Whether they appear to have meaningful review volume on Google, Yelp, and Indeed 3. Rank them by how useful they’d be for hiring research. The most useful competitors are established businesses with significant review volume over the last 6 months. 4. If you find fewer than 5 viable competitors, tell me — and suggest I expand the radius (for example: “I only found 3 at 10 miles. Try 15–20 miles to get a broader sample.”) 5. For each competitor on the list, provide ready-to-use search queries for each major review site. Format them like this: Bob’s Auto Repair, 1234 Main St - Google Reviews search: “Bob’s Auto Repair Anytown reviews” - Yelp search: “Bob’s Auto Repair Anytown yelp” - Indeed reviews: “Bob’s Auto Repair Anytown indeed reviews” I’ll paste those queries into Google and click through to each site to copy reviews. If you’re uncertain about a business or can’t verify current information, say so rather than guessing. Don’t invent businesses to fill out the list. ============================================================ THE QUESTION BUILDER PROMPT Chapter 1, Frankenstein Research Turns your Frankenstein attributes into structured T-Square interview questions, with legal guardrails built in. Before you paste: Use it in the same chat as your Quick Scan research so the AI has your attributes to work from. ------------------------------------------------------------ Based on everything we’ve discussed about this role and the local market in this conversation, I’m now ready to build the structured interview. Use the attributes we’ve identified — whether from the Quick Scan, from the reviews I pasted in, or from the full Frankenstein analysis we ran — as the basis for the question set. HARD CONSTRAINT — LEGAL COMPLIANCE Before designing any question, treat the following as off-limits. Do not write any question that asks the candidate about, requires the candidate to reveal, or makes any reference (direct or indirect) to: Age, date of birth, year of graduation, “near retirement,” or any language about generational difference (e.g., “older than you,” “much younger employee,” “set in their ways”) Race, color, ethnicity, ancestry, accent, or country of origin. Religion, religious practices, religious holidays, or church attendance. Sex, gender, gender identity, sexual orientation, or marital status. Pregnancy, parental status, family planning, or childcare arrangements Disability, medical conditions, mental health, prior workers’ comp claims, or any physical/cognitive characteristic. Genetic information or family medical history. Citizenship status (you may only ask if they are “legally authorized to work”) Arrest record (convictions may be askable depending on jurisdiction; arrests generally are not). Military discharge status. Credit history or financial status unless directly job-relevant (e.g., positions handling significant cash) If a question’s intent touches one of these categories, rewrite it to focus on the underlying behavior or skill without referencing the protected category at all. For example: Don’t ask: “Have you ever managed someone older than you?” Do ask: “Tell me about a time you implemented a new process with a team member who was resistant to change.” If you cannot test an attribute without referencing a protected category, flag it for me in your output and skip the question. Don’t try to work around the rule. QUESTION DESIGN RULES For each attribute, give me an open-ended interview question that: 1. Cannot be answered with yes or no. 2. Does not lead the candidate toward an obvious “right” answer (for example, do not ask “Do you arrive at work on time?” — ask them to describe their morning routine instead). 3. Forces the candidate to give a specific example, describe a scenario, or walk through how they would handle a situation. 4. Can be answered in 1–2 minutes (not a 10-minute essay question). 5. Reveals the attribute through the answer rather than asking about it directly. 6. Is resistant to AI-prepared answers — favor questions that require specific personal examples, real-time follow-up depth, or scenario walkthroughs that a rehearsed AI script couldn’t fake. 7. Where possible, ground the question in the actual local friction patterns we identified earlier in this conversation. If a competitor we discussed has a specific recurring problem (a pricing dispute, a quality control failure, a communication breakdown), use that real scenario in the question instead of a generic one. OUTPUT FORMAT For each attribute, give me: Context: the local friction pattern, attribute source, or operational reason this question exists The Question: one to two sentences, ready to ask Strong Answer (✓): what scores as a ✓ on the T-Square Weak Answer (✗): what scores as an ✗ on the T-Square Red Flag: what disqualifies the candidate entirely CAP AT 15 ATTRIBUTES If we identified more than 15 attributes earlier, prioritize the ones that came up across multiple competitors or showed up most often in review patterns. We’ll cap the question set at 15 to match the T-Square Score Sheet. ============================================================ THE HEADHUNTING PROMPT Chapter 1, Bonus Time! Reviews competitor customer reviews for employees named by customers and ranks them as possible headhunting targets. Before you paste: Change the role (Assistant Manager) and the business type and area (dry cleaning and laundry, Covington / Mandeville) to match your search. Use it in the same chat, after you paste in the reviews. ------------------------------------------------------------ Act as a hiring research scout. Review the provided customer review data for local competitors in my area to identify potential headhunting targets for an Assistant Manager role. Data & Context: - Role: Assistant Manager - Input: Customer reviews for local dry cleaning and laundry competitors (Covington / Mandeville area) Constraints & Rules: 1. Target Selection: Only evaluate non-owner employees who are specifically mentioned by name in customer reviews. Strictly exclude business owners or founders. 2. Ranking: Rank the top candidates from top to bottom based on customer feedback and ratings. 3. Attribute Alignment: Evaluate each candidate against key operational attributes (e.g., hospitality and client retention, culture and integrity, performance under pressure). 4. Evidence: For every ranked individual, cite specific customer cases or quotes from the review data where the customer highlighted them excelling. 5. Accuracy: Only use names and quotes that actually appear in the review data I provided. If you can’t find enough, say so. Don’t invent people or quotes to fill out the list. Output Format: - Ranked List (Top to Bottom): For each candidate, provide: - Full/Given Name & Current Location - Overall Rank & Role Category - Attribute Strengths (Why to headhunt) - Customer Cases Excelling (Specific review examples/quotes) - Reason for Rank - Summary Headhunting Matrix: A structured table listing Rank, Potential Talent, Current Location, and Key Strengths.