The Gap Between Demo and Production
What training a new employee taught me about training AI agents — and why your pilot is stuck.
THE ADVANTAGE
Every AI demo works. Almost no AI workflow ships.
I've spent the last month rebuilding the pipelines that run my podcast and my newsletters — ways to automate the research, let me add my take, and take a publication or an episode all the way to finished. It's a lot harder than you'd think. Not because the models aren't smart. Because 80% is worthless when you have to ship the same thing 200 times a year.
The way I think about it: I'm training a new employee. Like any new hire, the first week they get most things right and a bunch of things wrong. You give direction. You correct. You update the handbook. Week two is better. Week three is better than that. That's the work — and there's no shortcut.
The 80-to-100 gap is not a model problem. It's a context problem. Every piece of tribal knowledge your team carries in its head — voice rules, edge cases, how you link sources, the twelve exceptions nobody wrote down — has to become context an agent can read. Otherwise it drifts. (Mine does, all the time.) When drift happens, you do two things: update the agent's memory in the moment (“hey, remember this”), and update your source of truth so it doesn't repeat next week.
Here's the actual advantage. I write everything to a human-readable source of truth — Notion, in my case — and sync it to GitHub so my agents can pick up whatever changed. If a better tool comes out next year, I'm not rebuilding. I point a different agent at the same source. The context is mine. The tool is interchangeable. That's the moat.
Join us on Friday, April 24, 9:00–10:00 AM ET at The Loading Dock for a Raleigh Durham Startup Week session with Mark Hinkle on why RTP is uniquely positioned to build the next generation of AI-native startups. The conversation will cover how AI is changing early-stage company building, what use cases are working now, and why founders can do more with less than ever before.
TRY THIS NOW
Pick one workflow you run more than 10 times a quarter. An onboarding email. A weekly status update. A research brief. A social post. Then:
Run it through Claude or ChatGPT with just a task description. Note what's wrong — tone, format, missing detail, hallucinated fact. That's your 80%.
Write down every correction in plain English. Voice rules, good examples, edge cases, the “obvious” stuff nobody told the model. That's your context gap.
Save it in a Project (ChatGPT) or a Claude Project, re-run the same task, and measure. The delta between run one and run two is your ROI on context engineering.
PROMPT OF THE WEEK
Turn any repeatable workflow into a context-engineered template.
I'm going to share a task I do repeatedly: [DESCRIBE THE TASK].
Here are 3 examples of good outputs I've produced for this task:
[PASTE EXAMPLE 1]
[PASTE EXAMPLE 2]
[PASTE EXAMPLE 3]
Here are my non-negotiable rules:
- [RULE 1 — voice, format, length, etc.]
- [RULE 2]
- [RULE 3]
Here are my edge cases and how I handle them:
- [EDGE CASE 1: how I handle it]
- [EDGE CASE 2: how I handle it]
Based on these examples and rules, produce a reusable instruction set I can save as a Project so this task runs 100% repeatable every time. Include a checklist I can paste at the top of each run to verify the output matches my standards.THE EDGE
Only 28% of enterprise AI projects fully meet ROI expectations, according to Gartner's April survey of 782 I&O leaders. The top two failure modes — data quality and skill gaps — tied at 38% each. Nothing about the models. If three-quarters of your peers' pilots are stalled, the fix isn't a better LLM. It's closing the context gap before you scale the rollout.
Keep learning with these upcoming events from the All Things AI community.
April 23rd | Happy Hour at Raleigh-Durham Startup Week 2026 | Join Mark Hinkle, founder of All Things AI, and Erik Troan, co-founder and CTO of Pendo, for a discussion on how great products are shaped by what users actually say and do.
May 6th | Linkedin Live | Why Jensen Huang's Betting on Confidential Computing in the AI Factory — In this session, Mark Hinkle sits down with Aaron Fulkerson, CEO of Opaque Systems — the leading Confidential AI platform born from UC Berkeley's RISELab and backed by Intel, Accenture, and many others — for a conversation that will fundamentally change how you think about enterprise AI.
Forward this to the VP who just asked “why is our AI pilot stalled?” — the answer isn't the model.
P.S. You can watch this play out in real time. The first seven episodes of my Rogue Agents podcast are me doing exactly this: training the pipeline every week, correcting, feeding the source of truth, closing the gap. By episode seven we're closer to my threshold of “good enough.” Not perfect. Repeatable. That's the bar.
I appreciate your support.
Your AI Sherpa,
Mark R. Hinkle
Publisher, The AIE Network
Connect with me on LinkedIn
Follow Me on Twitter
