# [[AI in DevRel]]
![[AI in DevRel.svg]]
The proliferation of [[Artificial Intelligence]] brings up some interesting considerations for [[Developer Relations]] work: we can use AI to do the work we've always done, but we will also need to adjust the work itself because our users may be using AIs.
## Using AI in DevRel work
You can use AI in the course of doing DevRel activities:
- building demos or writing code ([[GitHub Copilot]], [[ChatGPT]])
- learning and summarising information
- triaging or answering support questions based on documentation and content ([[Innkeep]])
- Writing CFPs
### Mitigating downsides
If you're using LLMs in your work, consider ways that you might shore up on known weaknesses of AI:
- Narrow down the focus of the channels you want to have an LLM parse. Meet devs where they are. (Docs? Discord? Slack? GitHub?)
- Be transparent about the use of AI. Disclose when people are talking to a bot. Build in feedback mechanisms.
- Mine AI conversations for insights. Treat conversations with AI as searches.
## DevRelling with AI in mind
If we're using AI, the chances are pretty high that our products' potential users are using AI, too. In that case, we should start to think about what it means to have an AI, like an [[Large Language Model|LLM]], as an audience. The following tips help us think about how we might change the way we do things to make our work more palatable to an AI. [^gomez]
- Avoid [[Single-Page Architecture|SPA]] web apps and monoliths-- allow scrapers.
- Add context (such as version numbers) when talking about features.
- Prefer heavily structured content (headings).
- Define acronyms.
- Make tutorials more scenario-based rather than concepts-based.
[^gomez]: Gomez, N. (2024). *How LLMs are changing developer expectations.* Talk given at DevRelCon 2024 in New York. [[DevRelCon Day 1 - Lightning Talks#DevRel & AI How LLMs are changing developer expectations]].
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