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title: LLMs reward expertise | daily.dev
description: Domain expertise is the most important factor in getting value from LLMs, not generic prompting tricks. Using Terence Tao's ChatGPT conversation about the...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# LLMs reward expertise

**[sean goedecke](https://daily.dev/sources/seangoedecke)** · 5 min read · 19 upvotes · 2 comments

## Summary

Domain expertise is the most important factor in getting value from LLMs, not generic prompting tricks. Using Terence Tao's ChatGPT conversation about the Jacobian Conjecture as an example, the author shows that experts extract far more from models because they can steer conversations, identify wrong answers, and suggest better approaches. The same principle applies to software development: deep familiarity with a codebase lets developers push LLMs much harder than novices can. This suggests human expertise remains valuable even as models improve, because the bottleneck is often the human's ability to communicate precisely what solution they want.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://seangoedecke.com/llms-reward-expertise>

## Community discussion

Top comments from developers on daily.dev.

**@confidentcoding** · 3 upvotes

> If these tools _reward expertise_, yet the tools can _actively circumvent_ the friction that _cultivates expertise_, then what is the path for one to _become an expert_ so they can effectively [use these tools](https://daily.dev/posts/ai-coding-will-prevent-expertise-the-need-for-ongoing-friction-in-long-term-skill-formation--qiv3i2hlk)?

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-coding](https://daily.dev/tags/ai-coding), [#prompt-engineering](https://daily.dev/tags/prompt-engineering)

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