eval-driven development
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Monetizing AI with Confidence: Proven Models, Smart Pricing, and ROI You Can Defend
Turning AI features into sustainable revenue requires more than a great demo; it demands crisp packaging, pricing discipline, and visible customer outcomes. I outline practical models—add-ons,…
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How to Structure Prompts for a Reliable AI Resume Coach
A practical prompt architecture for building an AI resume coach that stays grounded in evidence, rewrites consistently, and can be tested before release.
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Trustworthy AI Product Engineering: From Demo to Daily Use
A practical operating system for making AI outputs traceable, uncertainty actionable, failures bounded, and quality measurable in customer workflows.
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How to Design Multi-Agent Fintech Support That Finishes Work
A field guide to dividing agent roles, controlling multi-day fintech cases, enforcing compliance, and measuring verified resolution instead of chat volume.
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AI Product Management Skills: A Practical 12-Month Roadmap
A 12-month roadmap for building AI literacy, evaluating prototypes, shipping reliable workflows, and scaling product governance.
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Context-Driven AI Product Engineering That Survives Production
Learn how to design context contracts, retrieval pipelines, evaluations, and ownership so an AI feature stays grounded and useful in production.
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AI in Product Design: My Proven Playbook, Real Use Cases, and the Tools That Win Faster
AI in product design has moved from novelty to necessity, and the fastest-moving teams are weaving it through discovery, prototyping, and validation. I share a practical…
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From Concierge to AI Marketing Engine: Inside Mowie’s Document Hierarchy Playbook
SMBs don’t need a bigger team to market like pros—they need smarter systems. In this first-person breakdown, I unpack how Mowie evolved from a hands-on concierge…
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Operationalizing AI: A Practical System for Scalable Growth
A practical operating model for choosing AI use cases, designing controlled workflows, measuring value, governing risk, and scaling what works.
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How to Build a Self-Improving AI Support Operation
A practical operating model for turning AI support failures into owned, tested changes that improve resolution without weakening quality or trust.
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Beyond Accuracy: The Trust-First Evaluation Metrics I Use to Scale High-Impact AI Products
Trust—not accuracy—determines whether AI features earn adoption, retention, and long-term impact. In this piece, I share the layered metrics I use to evaluate model quality and…
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From No-Code Hack to 10,000 Weekly Calls: Inside Perk’s Voice AI That Actually Works
A small Perk team turned a Make.com hackathon into a voice AI agent that makes 10,000+ weekly hotel verification calls—reducing failed virtual card payments and traveler…
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How Startups Earn Visibility in ChatGPT and Perplexity
A practical system for turning buyer questions, structured pages, independent proof, and repeatable evaluations into startup visibility in AI search.
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Evidence-Driven Product Analytics: From Signal to Decision
A practical operating system for turning behavioral signals into sound hypotheses, reliable experiments, and product decisions your team can defend.
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How to Run AI-Augmented Workflow Experiments That Matter
A practical playbook for testing AI-assisted workflows with clear hypotheses, full-job metrics, guardrails, and evidence-based autonomy decisions.
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How to Evaluate AI Voice Support in Real-World Conditions
A practical framework for testing AI voice support across live conversation, backend actions, failure recovery, escalation, and rollout readiness.
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Agentic AI for Incident Response: A Practical Operating Model
A practical operating model for scoping, governing, evaluating, and rolling out incident-response agents without surrendering human control.
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How to Build an Evaluation-Driven AI Innovation Strategy
Build an AI innovation system that links portfolio bets to customer outcomes, release gates, and clear evidence for killing, refining, or scaling them.
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How to Operationalize AI: A Practical Adoption Playbook
A field-ready playbook for choosing AI workflows, securing their data, evaluating reliability, and expanding autonomy only when outcomes justify it.
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Evidence-Driven AI Product Delivery: A Practical Operating Model
A practical operating model for choosing AI bets, defining proof before build, evaluating releases, and scaling only when outcomes and guardrails hold.
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Browse topics
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