Benchmarking Conversational Analytics: Looker Semantic Layer vs. Direct SQL
Semantic Layer Importance in Agentic AI
There is a continued discussion over the need for a semantic layer to support enterprise scale deployments of conversational analytics and agentic data applications. Frequently referenced is a 2024 paper using GPT 4, which is one of the few quantitative assessments published in the public domain demonstrating the value of a semantic layer. The state of LLMs, including reasoning improvements and context adherence, continues to evolve, and existing benchmarks such as this quickly turn stale.
Recently, DBT put out a blog post with their own updated 2026 benchmark comparing Text-to-SQL against the DBT semantic layer, highlighting the benefits of the semantic layer. Unfortunately, this benchmark had limitations on the scope of the evaluation (e.g., only using 11 questions from the source benchmark, minimal context engineering).
This study set out to expand on the prior art by leveraging the same dataset, but with an expanded set of questions and a more comprehensive evaluation harness.