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The session's method is stated plainly enough to be quoted against it later: choosing the right model for an agent is guess and check — the scientific method (4:46).
That is more honest than most guidance in this area, and the walkthrough that follows is the useful part, because it puts numbers on each step of a process usually described qualitatively.
The gap between working and shippable
The starting position is a customer service agent handling product returns for a retailer. Out of the box, quality sits at 76 per cent (12:00).
Their framing of that number is the whole session: not bad, not great, and not shippable. Production needs 90 per cent or better.
Anyone who has taken a prototype toward deployment recognises this precisely. The first result is encouraging and arrives quickly. The remaining distance is where the effort lives, and it is not covered by a better prompt.
What they do about it
The progression is instructive because it is ordered by cost rather than by sophistication.
Show the model a pattern to imitate — here is how a customer response should look (17:52). This is cheap, immediate, and takes you some distance.
Then supervised fine-tuning of a smaller model, with the scores moving as you go (31:01). The specific detail worth keeping is that they discuss a run where results were mixed — some passed, some failed, the aggregate still short — and treat that as information rather than failure. Mixed results are the normal state partway through this process, and reading them requires the measurement discipline other sessions at this conference argue for.
For domains with checkable answers, the reinforcement approach they describe is generating solutions, scoring against verifiable ground truth, and reinforcing the better ones — which works for mathematics and code precisely because the ground truth exists.
The teaching order
The observation most likely to survive is about curriculum, and it is counterintuitive if you think of these systems as databases.
You do not teach the hardest problem first. To teach arithmetic, you begin with addition and subtraction (33:44).
That models improve on a graded sequence rather than absorbing a corpus is a claim about learning rather than storage, and it has a practical consequence: the ordering of your training data is a design decision, not a preprocessing detail. Teams that assemble a dataset and shuffle it are discarding a variable that matters.
What it costs to take this seriously
The implicit argument runs against the industry's dominant instinct.
The easy path is to use the largest available model, accept its cost, and adjust prompts when results disappoint. The path described here is measurement, then fine-tuning a smaller model, then reinforcement where ground truth exists — arriving at better quality at lower serving cost, having spent substantially more engineering effort to get there.
Which is the right trade depends entirely on volume. At low volume the effort dominates and the large model wins. At production volume the serving cost dominates and the effort amortises. The session does not put that boundary in numbers, and it is the number a team would most want before committing.
Key numbers
- 76% → 90%
- starting quality of an off-the-shelf agent against the threshold they treat as shippable 12:00
Talk chapters
Key takeaways
- 01
Their stated method for model selection is guess and check, which is more honest than most guidance in this area. 4:46
- 02
A returns-handling agent starts at 76 per cent quality, which they frame as not shippable against a production threshold of 90 or better. 12:00
- 03
The cheapest first move is showing the model a pattern to imitate rather than describing what you want. 17:52
- 04
Mixed fine-tuning results — some passing, some failing, the aggregate still short — are treated as information rather than failure. 31:01
- 05
For checkable domains the loop is generate, score against verifiable ground truth, reinforce the better ones. 33:44
Entities mentioned
Organizations
Related talks
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