This analysis set out to test the common but under-proven intuition that a strong type system helps LLM agents produce more correct results, running a controlled benchmark that kept the AI model, underlying dataset and set of test questions consistent while swapping between the #SQL, Cypher and TypeQL query languages to isolate this effect. While #SQL had the highest single-shot success rate, TypeQL’s robust type checker caused 93% of its incorrect queries to throw clear visible errors, compared to just 5% for #SQL and 40% for Cypher, and when paired with a simple retry loop that feeds these errors back to the LLM to fix, TypeQL ultimately achieved the highest overall query accuracy.
https://typedb.com/...eneration-across-sql-cypher-and-typeql
Benchmarking LLM query generation across SQL, Cypher and TypeQL
Explore our new LLM benchmark comparing MySQL, Neo4j, and TypeDB. Learn how context and retry budgets help AI agents self-correct and improve performance.