How I AIHow custom GPTs can make you a better manager | Hilary Gridley (Head of Core Product at Whoop)
CHAPTERS
Why managers should build custom GPTs (and what they replace vs. don’t)
Claire frames the episode around a gap: most AI talk focuses on individual contributors, but managers can gain huge leverage too. Hilary sets expectations—GPTs won’t replace great managers, but they can reliably take work from “0 to 60–70%,” freeing time for strategy and coaching.
Getting a GPT to “think like you” by articulating what ‘good’ looks like
Hilary explains that great management often hinges on clearly communicating standards—something many leaders feel but can’t articulate. Her GPT-building starts with extracting her implicit taste into explicit criteria that others (and a model) can use.
Demo: Reverse-engineering your preferences with good vs. bad examples
Hilary demonstrates a low-tech but effective method: collect “before/after” examples (bad → edited good) and have the model infer the criteria. She prefers initially vague prompts to avoid biasing the model, then tightens specificity later.
Turning inferred patterns into a usable rubric (and making it ‘100x more specific’)
Once the model proposes slide-quality criteria, Hilary pushes it to be dramatically more concrete using her signature prompt: “Be 100 times more specific.” The output becomes a manager-friendly rubric: unambiguous, operational, and ready to reuse.
Why articulating taste matters for leadership and employee experience
Claire connects the workflow to broader management tasks—design feedback, hiring, performance evaluation, writing—arguing rubrics reduce frustration and speed growth. Hilary adds that managers often lack bandwidth to explain “why,” and AI can provide patient, always-available coaching.
Demo: Building the ‘Deck Doctor’ slide-deck evaluator GPT
Hilary turns the rubric into a custom GPT by asking ChatGPT to write the GPT’s own system prompt. She emphasizes role clarity (“my job vs. your job”) and encourages the GPT to be “ruthlessly helpful,” avoiding empty praise.
Testing the GPT on a real deck and iterating on feedback quality
Hilary tests by uploading a PDF deck and reviewing the scored rubric output. She treats early versions as “good enough,” then iterates based on usefulness rather than perfection—only improving what people actually adopt.
Rolling GPTs out across the team: beta tests, virality, and personalization
Hilary explains her adoption strategy: start with one teammate, expand if it sticks, and let useful GPTs spread organically. She also tailors GPTs to individual coaching needs—e.g., anticipating exec questions for a specific presenter.
Manager coaching use case: AI to improve writing and strategic influence
Hilary shows a writing workflow she teaches her team: write a rough draft, then ask AI to restate the thesis and supporting points to test clarity. She uses AI to challenge arguments (“blind spots”) and restructure for clarity—while keeping the final wording her own.
Lightning round: women and AI adoption, fun personal use cases, and ‘model triangulation’
Hilary shares concern that women—especially high-achieving women—are adopting AI tools more slowly, which could widen opportunity gaps. She also highlights playful non-work uses (reading companion, craft shopping lists) and admits she “pits” models against each other when one won’t comply.
Where to follow Hilary and go deeper on ‘super manager’ AI tactics
Hilary shares resources for learning more: her Substack, a Maven course on AI management, and a women-in-AI community initiative. Claire closes with standard show outro and ways to subscribe and review.
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