PRESENTS · SESSION 4 · PART 1 · START WITH THE PROBLEM SAT JUN 13, 2026 · 12:00 PM EDT Start with the problem Think first, then AI. Get the problem right before you reach for a tool. Today: how to take any HR problem and turn it into a spec. CO-INSTRUCTORS S Sandhiya Thiruvengadam Leader, HRBP & AI · Apexon Q Q Hamirani Chief People Officer, Highlevel · Adjunct Faculty, LBS Session 4 · Part 1 · Slide 1/18
YOUR CLIMB SO FAR Look how far you've come. From a chat box to a working tool. Chat, then Cowork, then Code. WEEK 1 a 2x2 on a Friday WITH CLAUDE WEEKS 2 TO 3 a skill that writes like you CLAUDE + COWORK LAST WEEK a working tool built in an afternoon COWORK + CODE ↩ HOME Session 4 · Part 1 · Slide 2/18
Everyone's already moving. In different directions, each chasing their own definition of done. PAUL TRIED a JD rewriter DONE MEANS every edit explained, not just made NANCY TRIED a manager FAQ DONE MEANS the right answer, the first time ARJEN TRIED a reviews agent DONE MEANS reviews grounded in one leadership model ZAINAB TRIED a first-draft skill DONE MEANS output she sends with light edits OLIVIA TRIED an onboarding nudge DONE MEANS managers self-serve the onboarding plan ...and the rest of you are mid-build. Everyone's "done" looks a little different. That definition of done is the thing we put on the page today. ↩ HOME Session 4 · Part 1 · Slide 3/18
Today, everyone gets on the ramp. Not a new tool. You climb it with your problems, using whatever AI you already have. WHERE IT LEADS your spec ANY TOOL GETS YOU UP Claude · ChatGPT · Microsoft Copilot · Gemini Pick the one your company already pays for. The climb is the same. You start somewhere. You won't get it right every time. Each push up makes it better. ↩ HOME Session 4 · Part 1 · Slide 4/18
Three weeks, every door. Today, you point all of it at one problem. Claude Gemini ChatGPT Copilot skills · routines · schedule · chat · cowork · code · and some of you, a git We heard you. So today: one spec, written before any tool. ↩ HOME Session 4 · Part 1 · Slide 5/18
The questions you all asked. Every one of these is normal. You are not behind. SO MANY QUESTIONS ? ? ? ? ? all of them, completely normal "Do I even need the terminal?" "Can you show the skill behind it?" "Does my computer have to stay on for it to run?" "Why Gemini, not Claude?" "How is a Gem different from a chat?" "Skill, or artifact?" "When do I split one big skill?" "Do things carry between chats?" UNDER ALL OF THEM, ONE QUESTION Where do I start? Today, you start with a spec. ↩ HOME Session 4 · Part 1 · Slide 6/18
The ramp is the shape. The same three steps up: where it starts, what kicks it off, where it lands. STEP 1 · INPUT where it starts the sheet, files, data STEP 2 · TRIGGER what kicks it off a schedule, a status, a click STEP 3 · OUTPUT where it lands a nudge, a doc, an answer SAME THREE STEPS, WHATEVER YOU CLIMB WITH Claude · ChatGPT · Microsoft Copilot · Gemini A map, not a manual. You give the direction, not every footstep. Confidence is not control. ↩ HOME Session 4 · Part 1 · Slide 7/18
You already learned the pieces A spec just puts them in one place. You met these in Session 2, building agents in Copilot and Gemini. BLOCK 1 Instructions IN YOUR SPEC What you want done, and the rules it must follow. "Here's the job, and here are my rules." BLOCK 2 Knowledge IN YOUR SPEC What the AI must see: your policies, files, and data. "Read this before you answer me." BLOCK 3 Memory IN YOUR SPEC What done looks like, held so it doesn't drift. "Remember what we agreed good is." BLOCK 4 Tools IN YOUR SPEC What it's allowed to touch, and where the output goes. "You may use Slack. Send it there." + THE SHAPE Input · Trigger · Output INPUT what it starts from TRIGGER what kicks it off OUTPUT where it lands from Week 3 THE HARNESS A spec is a map, not a manual. You set the direction and where to grab the reins. Confidence is not control. BUILT IN, NOT BOLTED ON "Never rank down someone on parental leave" is a line in your spec, not something you catch after it ships. ↩ HOME Session 4 · Part 1 · Slide 8/18
Start with a spec, not a prompt. A spec gets your definition of done out of your head and onto the page. THE PART YOU ALREADY KNOW A spec is a policy document for your AI. You write policies every day. You already know how to do this. A PROMPT "Write onboarding emails." Vague. The rest is still in your head. A SPEC Who. What input. What good looks like. What's out of bounds. On the page, before you build. "Prompting is delegation, not commanding. The AI was never the bottleneck. My thin instructions were." PAUL · COHORT 2 · LAST WEEK'S REFLECTION ↩ HOME Session 4 · Part 1 · Slide 9/18
The spec first. The tool after. The wrong tool automates the wrong problem. The right tool amplifies the right process. THE OLD WAY · TOOL FIRST Pick the shiny tool. Then bend the problem to fit it. You automate the wrong thing, beautifully. TODAY · PROBLEM FIRST Write the spec. Then ask: which tool fits THIS? The tool disappears. The work gets done. ONCE YOU HAVE THE SPEC · WHICH SURFACE FITS? One-off, personal just talk it through → Chat or Cowork Repeatable, shareable runs again, others use it → Code, or an agent Needs your real data reaches your systems → connectors · Part 2 ↩ HOME Session 4 · Part 1 · Slide 10/18
Watch what happens with no spec A real one. This happened while building today's deck. a real conversation · no spec given YOU github.com/AlmostTechHR/Global-People-Labs.git CLAUDE "What would you like me to do with this repo?" YOU "read it" CLAUDE "Read it for what? Which files? To what end?" YOU "...we are going to work on week 4" CLAUDE "Which week 4? There are two copies. Doing what?" Three rounds. Nothing built yet. Time and tokens, gone. WHY IT KEPT ASKING The AI isn't slow. It's missing your definition of done. That lives in your head. Until you hand it over, every turn is a guess. THE LESSON Be close on what you want before you spend a single token. ↩ HOME Session 4 · Part 1 · Slide 11/18
First question: should this even be AI? Arjen said it in the room: "Not everything needs AI. Sometimes a plain integration is better." HIGH IMPACT LOW IMPACT LOW EFFORT HIGH EFFORT QUICK WINS Do these first. JD drafts · meeting notes · FAQ bot STRATEGIC BETS Plan for these. pay equity · attrition signals CONVENIENCE Nice, not urgent. tidy a doc · reformat a list RECONSIDER Maybe not AI at all. a plain system-to-system integration YOUR FRAMEWORKS You already have the toolkit: IMPACT spotting where AI helps SCALE choosing a vendor or tool CLEAR governing the data The 2x2 is just the first cut. YOU ALREADY DREW THIS in Week 1. Before you write the spec, place the task on the grid. If it lands bottom-right, the best AI move is no AI. ↩ HOME Session 4 · Part 1 · Slide 12/18
A spec, written out in full. Olivia's onboarding nudge. Notice: plain English, no code. Just the decisions only she can make. OLIVIA'S SPEC · MANAGER ONBOARDING NUDGE SPEC nudge each manager to finish their new hire's onboarding plan INPUT the new-hire list (HRIS or a sheet) + each hire's manager TRIGGER anyone not done by Friday OUTPUT a short, friendly nudge in Slack (or email) KNOWLEDGE your onboarding template + the 60-day policy DONE every new hire has a filled plan within 60 days NOT ALLOWED never nudge someone on leave; never show another hire's details Hand this to any tool. It now knows what you want, and what you won't allow. WHY IT WORKS Every line answers a question the AI would otherwise guess: INPUT → where do I look? TRIGGER → when do I act? OUTPUT → where does it land? DONE → when am I finished? NOT ALLOWED → never do what? No guesses left. No wasted turns. ↩ HOME Session 4 · Part 1 · Slide 13/18
Your turn. Sketch one spec. On paper. Pick one real task. No laptop. Just the shape. 7 minutes. A WORKED EXAMPLE · A PEOPLE LEADER · COHORT 2 Nudge managers to finish onboarding SPEC every new hire has a filled 60-day plan INPUT the new-hire list (from the HRIS or a sheet) TRIGGER anyone not done by Friday OUTPUT a short nudge in Slack or email KNOWLEDGE your onboarding template + policy DONE a filled plan for every hire inside 60 days NOT ALLOWED don't nudge anyone on leave Notice: no code. Just what you want, and what you won't allow. NOW YOURS · FILL THE BLANKS SPEC I want ______________________ INPUT it starts from ______________ TRIGGER it kicks off when __________ OUTPUT it lands in ________________ KNOWLEDGE it must read ____________ DONE good looks like _____________ NOT ALLOWED never ____________ Reflection: where in your week is the thing only you know how to define? INTERMEDIATE → ADVANCED Next: from framing to building. MCP is just how your spec reaches real data. ↩ HOME Session 4 · Part 1 · Slide 14/18
So what can you actually build? Real HR use cases, from teams in the field. AI now touches 43% of HR work. Not one of these a website. Resume screeningscored to a rubric, far faster Candidate sourcingscans the whole web, not just boards Onboarding Q&Aanswers new hires from your docs Offer letters + interview kitsdrafted from the scorecard Survey & sentimentthemes from open-ended feedback Policy + people Q&Aanswers straight from the handbook ONE SPEC TO START All HR. Each began as a spec, not a website. BEYOND HR · THE SAME MOVE Finance flags invoice mismatches · Marketing scrapes competitor prices · Ops sorts messy files by content. ↩ HOME Session 4 · Part 1 · Slide 15/18
And we just built one. Live. Olivia wanted managers to finish onboarding plans. No website. Just a spec, pointed at real tools. HOW IT RUNS The spec written first, before any tool A Google Sheet the input, where it starts The Friday check the trigger, what kicks it off A Slack nudge the output, where it lands THE REAL THING the real sheet, and the AI reasoning over it WHAT WE COULD NOT (YET) On a free Slack plan, asking Claude inside the channel needs a paid plan. So for now the channel self-serves, and the AI runs from Claude itself. Same spec you just learned. Let's go see it live. ↩ HOME Session 4 · Part 1 · Slide 16/18
No data yet? Draw it. A move for any build: no real data yet? Ask Claude to generate a realistic sample. Olivia's nudge needed a manager sheet she didn't have. HER SPEC SAID INPUT a list of managers, who is onboarding, and their status. The real sheet? Not ready yet. That stops most people here. It doesn't have to. THE MOVE Ask Claude to generate a sample. onboarding-sample.csv · generated, not real MANAGER NEW HIRE DAY STATUS Priya R.Sam OkaforDay 12done Marcus L.Dana KimDay 30pending Elena V.Tom ReyesDay 5done Raj P.Mia ChenDay 45pending Nadia H.Leo ParkDay 18done Omar S.Ava SinghDay 60pending Fake names, real shape. Three pending, three done, enough to build and demo the nudge today. Build against this now. Swap in the real sheet when it exists. The spec never changes. ↩ HOME Session 4 · Part 1 · Slide 17/18
Governance isn't a separate step. It lives in your spec. THE LINE THAT SAYS "NOT ALLOWED" You wrote a data-sensitivity grid last week. Those rules become lines in the spec: never rank down someone on parental leave never show payroll to a manager-facing tool never store an individual survey response never touch health or leave records Paul sorted 8 data types. Nancy sorted 9. That work is your guardrail list. Write the limit in before you build, not after. WHAT THE AI MAY AND MAY NOT TOUCH PUBLIC policy docs · go ahead INTERNAL JDs · resumes · with care CONFIDENTIAL psychometrics · aggregate only RESTRICTED payroll · health · leave · keep out The spec names the level. The AI stays inside it. ↩ HOME Session 4 · Part 1 · Slide 18/18
You've got your spec. Now wire it up. PART 2 · WIRE IT UP · GO DEEPER AT YOUR OWN PACE Where your spec meets real data Connectors, MCP, deploy, and making the case. The restaurant kitchen opens here. OPEN PART 2 → Reflection: where in your week is the thing only you know how to define? Weekend nudge: try ChatPRD. It interviews you and writes the spec for you. Bring one back next week. And when it works: bundle your skills into a plugin and hand them to your whole team. That's Part 2. ↩ HOME End of Part 1 · Part 2 next
SESSION 4 · PART 1 · COHORT 2

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