What Shreya Shankar thinks about LLMs
Computer scientist working on LLMs in data systems; computer science PhD at UC Berkeley, creator of DocETL, co-teacher of an AI evals course with Hamel Husain, and incoming assistant professor at Carnegie Mellon.
Shreya Shankar did not write this page.
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3 dated positions, 2025, in their own words. Our reading of what Shreya Shankar has said — not written or endorsed by them.
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Their wordsFor complex tasks, in the pre-LLM era, generating output was expensive but verification was comparatively cheap. You put in the work to produce something—a document, a piece of code, an analysis—and checking it was tractable.
↗On the Consumption of AI-Generated Content at Scalesh-reya.com 2nd of 3 in this piece
- 6 months earlier
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Their wordsLLMs often overuse bullet points, especially nested ones. Lists help when items are parallel and independent, but when ideas are connected or need context, a paragraph is usually better.
↗Writing in the Age of LLMssh-reya.com 2nd of 3 in this piece
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Our readingLLMs cannot tell what knowledge the reader already has, so a human writer has to fill the gap.
Their wordsIn summary, LLMs can’t reliably distinguish what’s assumed knowledge and what needs explanation, so they often gloss over the hard parts, and a human writer has to fill that gap.
↗Writing in the Age of LLMssh-reya.com 3rd of 3 in this piece