AI at work: design the workflow
Your team wants to 'use AI' for a real task, and the instinct is to reach for a bigger model. But that rarely fixes the actual problem.
The idea inside
Match the piece to the problem: private knowledge needs retrieval, a high-stakes action needs a human review gate, and a pure transform just needs a clear output shape. A bigger model is almost never the make-or-break piece.
After this lesson
You can look at a real work task and pick the design choice that makes or breaks it: retrieval for private knowledge, a human gate for risky actions, structure for transforms, instead of defaulting to a bigger model.
Where it leads
You matched jobs to design choices. Now meet the full cast of models you'd wire in.
Inside this lesson
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What this lesson shows
Match the piece to the problem: private knowledge needs retrieval, a high-stakes action needs a human review gate, and a pure transform just needs a clear output shape. A bigger model is almost never the make-or-break piece.
The question it opens with
Your team wants to 'use AI' for a real task, and the instinct is to reach for a bigger model. But that rarely fixes the actual problem.
The walkthrough, in the lesson's own words
- That's the piece. Bring the next task to the bench.
- Not the make-or-break piece here. Read why, then look again.
- A real job task is on the bench. Pick the ONE piece that makes or breaks it.
- Work the bench: get the make-or-break piece right on at least 2 of the 3 tasks.
- Match the piece to the problem. A bigger model is almost never the answer.
- Most AI-at-work problems aren't a model problem. Private knowledge needs retrieval, a high-stakes action needs a human gate, and a pure transform just needs a clear output shape. Match the piece to the problem.
- Can a mistake do real harm or reach a customer?
- Is the answer already in front of it, just messy?
- Only if none of those fit is a bigger or different model the lever.
- Your team wants AI to answer support questions from your product docs and send the reply automatically. What two pieces does this workflow most need?
- Retrieval, so it answers from your actual docs instead of guessing, and a human review gate, because it sends to customers and a wrong auto-reply is costly. A bigger model addresses neither the missing knowledge nor the risk of an unreviewed send.
Key takeaway
You picked the make-or-break design choice for three real job tasks, and saw why retrieval, human review, or output structure beats 'use a bigger model'.
What you can do after this lesson
You can look at a real work task and pick the design choice that makes or breaks it: retrieval for private knowledge, a human gate for risky actions, structure for transforms, instead of defaulting to a bigger model.
Where it leads: You matched jobs to design choices. Now meet the full cast of models you'd wire in.
This is the written summary. The lesson itself is interactive: you predict, drag and operate the mechanism above, and the reveal answers you.