Making a dataset AI-ready is mostly a chunking and metadata problem, not a volume problem. A retrieval-augmented generation (RAG) system retrieves chunks of data and feeds them to a model -- if a chunk doesn't make sense on its own, the model can't use it well no matter how much data surrounds it.

Each record should be self-contained: readable and meaningful without the records before or after it. A glossary term with its own definition, example, and related-terms list is RAG-ready. A paragraph that says "as mentioned above" is not.

Add metadata that supports filtering and citation: a stable URL, a last-updated date, a category. This lets an AI system retrieve the right chunk and tell a user where it came from.

Finally, publish in a format a retrieval system can actually fetch: structured JSON files, a JSON-LD schema.org block on the page, and -- where it makes sense -- a dedicated machine-readable endpoint like an llm.txt file or a catalog JSON. None of this requires more data, just more structure around the data you already have.