Show HN: Tips for getting great Text2Cypher outputs from LLMs for Graph RAG

For folks working on Graph RAG and trying to get LLMs to generate Cypher queries, I ran some experiments on the LDBC dataset and wrote a blog post about it (code is available in the link shown at the end of the post). I've been trying to answer a burning question of mine that I've had for a while now: when doing Text2Cypher, are LLMs better at interpreting graph schemas in JSON, XML or YAML? (Spoiler alert, the format barely matters, it's all to do with context engineering and retaining only the relevant parts of the graph schema in the prompt). Results on the latest LLMs are really good!

The post also contains some other tips on graph schema design: I think we're in an age now where we need to design graph schema for both LLMs and humans. If you're working on Text2Cypher in any way, hope some of these ideas and experiments are useful!