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When initially experimenting with LLMs and agentic AI, software program engineers at Notion AI utilized superior code technology, complicated schemas, and heavy instructioning.
Rapidly, although, trial and error taught the staff that it may do away with all of that difficult information modeling. Notion’s AI engineering lead Ryan Nystrom and his staff 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 function: Cutomizable AI brokers — which have rapidly change into Notion’s most profitable AI instrument up to now. Based mostly on utilization patterns in comparison with earlier variations, Nystrom calls it a “step operate enchancment.”
“It's that feeling of when the product is being pulled out of you quite 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 function?’”
‘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, quite than codify guidelines of how brokers ought to behave in varied eventualities. The rationale: LLMs are designed to know, “see” and cause about content material the identical manner people can.
“Now, at any time when 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 they usually may perceive what's happening?” Nystrom stated on the podcast. “If not, it's going to do a nasty job.”
Stepping again from “fairly difficult rendering” of information inside Notion (akin to JSON or XML) Nystrom and his staff 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 enables the mannequin to work together with, learn, search and make adjustments to textual content recordsdata.
In the end, this required Notion to rewire its methods, with Nystrom’s staff focusing largely on the middleware transition layer.
In addition they recognized early on the significance of exercising restraint with regards to context. It’s tempting to load as a lot info right into a mannequin as doable, however that may gradual 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 likewise accuracy.”
A spartan strategy can be vital within the case of tooling; this may also help groups keep away from the “slippery slope” of limitless options, Nystrom suggested. Notion focuses on a “curated menu” of instruments quite than a voluminous Cheesecake Manufacturing unit-like menu that creates a paradox of selection for customers.
“When individuals ask for brand 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 have 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 remains 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 price efficient your AI function 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.
Subscribe to Past the Pilot on Apple Podcasts, Spotify, and YouTube.
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