When initially experimenting with LLMs and agentic AI, software program engineers at Notion AI utilized superior code technology, advanced schemas, and heavy instructioning.
Rapidly, although, trial and error taught the group that it may eliminate all of that sophisticated information modeling. Notion’s AI engineering lead Ryan Nystrom and his group pivoted to easy prompts, human-readable representations, minimal abstraction, and acquainted markdown codecs. The outcome was dramatically improved mannequin efficiency.
Making use of this re-wired strategy, the AI-native firm launched V3 of its productiveness software program in September. Its notable characteristic: Cutomizable AI brokers — which have rapidly turn out to be Notion’s most profitable AI instrument so far. Primarily based on utilization patterns in comparison with earlier variations, Nystrom calls it a “step perform enchancment.”
“It's that feeling of when the product is being pulled out of you moderately than you making an attempt to push,” Nystrom explains in a VB Past the Pilot podcast. “We knew from that second, actually early on, that we had one thing. Now it's, ‘How may I ever use Notion with out this characteristic?’”
‘Rewiring’ for the AI agent period
As a conventional software program engineer, Nystrom was used to “extraordinarily deterministic” experiences. However a lightweight bulb second got here when a colleague suggested him to easily describe his AI immediate as he would to a human, moderately than codify guidelines of how brokers ought to behave in varied eventualities. The rationale: LLMs are designed to know, “see” and purpose about content material the identical manner people can.
“Now, each time I'm working with AI, I’ll reread the prompts and power descriptions and [ask myself] is that this one thing I may give to an individual with no context and so they may perceive what's occurring?” Nystrom stated on the podcast. “If not, it's going to do a foul job.”
Stepping again from “fairly sophisticated rendering” of knowledge inside Notion (akin to JSON or XML) Nystrom and his group represented Notion pages as markdown, the favored device-agnostic markup language that defines construction and which means utilizing plain textual content with out the necessity for HTML tags or formal editors. This permits the mannequin to work together with, learn, search and make modifications to textual content recordsdata.
In the end, this required Notion to rewire its methods, with Nystrom’s group focusing largely on the middleware transition layer.
In addition they recognized early on the significance of exercising restraint on the subject of context. It’s tempting to load as a lot info right into a mannequin as attainable, however that may sluggish issues down and confuse the mannequin. For Notion, Nystrom described a 100,000 to 150,000 token restrict because the “candy spot.”
“There are instances the place you’ll be able to load tons and tons of content material into your context window and the mannequin will battle,” he stated. “The extra you set into the context window, you do see a degradation in efficiency, latency, and in addition accuracy.”
A spartan strategy can also be vital within the case of tooling; this may help groups keep away from the “slippery slope” of countless options, Nystrom suggested. Notion focuses on a “curated menu” of instruments moderately than a voluminous Cheesecake Manufacturing facility-like menu that creates a paradox of alternative for customers.
“When individuals ask for brand spanking new options, we may simply add a instrument to the mannequin or the agent,” he stated. However, “the extra instruments we add, the extra selections the mannequin has to make.”
The underside line: Channel the mannequin. Use APIs the way in which they had been meant for use. Don't attempt to be fancy, don't attempt to overcomplicate it. Use plain English.
Hearken to the total podcast to listen to about:
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Why AI continues to be within the pre-Blackberry, pre-iPhone period;
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The significance of "dogfooding" in product growth;
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Why you shouldn’t fear about how value efficient your AI characteristic is within the early phases — that may be optimized later;
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How engineering groups can maintain instruments minimal within the age of MCP;
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Notion’s evolution from wikis to full-blown AI assistants.
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