AI improvements

At the moment, LLMs are very dummy and not capable of generating correct AsyncAPI v3 documents.

My observation is that they internally generate v2 and call such documents v3, sometimes try some conversion, but the output is bad.

We need some basic quality improvements to make sure LLMs get proper context about AsyncAPI:
- make sure on each build we generate and expose `llms-full.txt` and `llms.txt`
- make sure it is added to https://llmstxt.site/ and https://directory.llmstxt.cloud/
- we have https://www.asyncapi.com/rss.xml but we need to explicitly also generate `sitemap.xml` (rss.xml is explicitly configured for Google Search, but Google Search is not the only consumer we need to satisfy) and `llms.txt` (LLM sitemap-like)

Last but not least, you need to explore:
- what approach should be applied to https://v2.asyncapi.com/. Do we change anything
- probably makes sense also to expose json schema? it is already in the website repo: https://github.com/asyncapi/website/blob/master/config/3.0.0.json

Above is just rough basics described by me. If you know any other latest-known techniques to improve AI-experience even more, please share.

Start by reviewing the website build configuration and the existing rss.xml generation, then inspect config/3.0.0.json and the v2.asyncapi.com site. Define what should be generated for llms-full.txt, llms.txt, sitemap.xml, and the JSON schema, including whether v2 needs changes. Done means the agreed artifacts are generated on each build and the external directory submissions are addressed.

Written by the indexing model from the issue text.