# semantic-view

Published articles for semantic-view.

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## Lessons learned from scaling data scientists with AI

DevFeed: [Lessons learned from scaling data scientists with AI](<https://devfeed.tech/articles/lessons-learned-from-scaling-data-scientists-with-ai-23711.md>)

Original publisher: [Read original article](<https://medium.com/whatnot-engineering/lessons-learned-from-scaling-data-scientists-with-ai-e7aa7b3235b4?source=rss----162aeca881b0---4>)

Author: Whatnot Engineering

Published: 2026-01-29T18:59:15Z

Content type: opinion

Language: en

Sources: [Whatnot Engineering](<https://devfeed.tech/sources/whatnot-engineering.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Slack](<https://devfeed.tech/topics/slack.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [SQL](<https://devfeed.tech/topics/sql.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [data](<https://devfeed.tech/tags/data.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [llms](<https://devfeed.tech/tags/llms.md>), [scope](<https://devfeed.tech/tags/scope.md>), [semantic-view](<https://devfeed.tech/tags/semantic-view.md>), [slack](<https://devfeed.tech/tags/slack.md>), [trust](<https://devfeed.tech/tags/trust.md>)

### AI overview

Whatnot describes lessons from deploying an LLM-powered Slack bot for data questions. The team limited access to pre-vetted datasets and data scientist-endorsed metrics to reduce incorrect answers and protect trust, while acknowledging that this conservative approach would miss some questions.

### Source excerpt

Alice Leach, Anthony Sedarous, Evan Hou, Faithful Alabi, Stephen Bailey, Tanisha Kore | Data "Can you check my SQL?" "Which table has the livestream data?" "How do I calculate GMV again?" At Whatnot, our data scientists were drowning in these sorts of questions. At the same time, leaders asking them might wait hours for answers that should take seconds. In 2025, we shipped an LLM-powered Slack bot to break this tension. The results were immediate: the help channel converted from "ask a data scientist" to "ask the bot" overnight. Since launch, employees have had over ten thousand data questions answered by AI systems, with most answered in tens of seconds and all deflected from the data team's queue. Widespread adoption (and proliferation outside of Slack) has revealed limitations that have forced us to rethink how we architect data access for AI systems. Sample conversation from the first verison of our Slackbot. Below, we share a few of the key lessons learned and how we plan to make our team even faster in the coming year. Lesson 1: Flexibility vs. trustworthiness is the key design tradeoff Data experts are valued because they are both trustworthy and flexible: ask them a question, and you can get back not just an answer but their confidence in it as well. LLMs, on the other hand, are famous for being confidently clueless. The first question facing a team employing them for data science work is, "How risky is a wrong answer?" It's not a new question: every data product provides consumers a degree of flexibility, whether that's almost none (public metrics) or a significant amount (raw or unstructured datasets). What's new is that, with AI interfaces, teams can control the consumer's full aperture. Many teams default to maximum flexibility by giving the LLM access to everything and letting it figure it out. If the goal is to replace experts, this is backwards. We went the opposite direction: locked down access to pre-vetted datasets, accepting that we'd miss around