LLMs struggle with generating correct table joins
Posted: Tue Feb 11, 2025 4:00 am
Schema Complexity: when database schemas become intricate.
Generative Inconsistencies: While LLMs are powerful at generating responses, they can produce inconsistent or incorrect results when handling calculations and KPIs.
Terminology Mismatch: Database tables and columns are often named according to data engineering conventions, which may differ from the business terminology used in queries. With context, LLMs france whatsapp number data find it easier to map between these different terminologies.
Addressing Challenges with a Semantic Layer
The primary use case for a universal semantic layer is to allow enterprises to democratize data access with accuracy, safety, and consistency. While this initial value proposition is compelling enough on its own, the semantic layer also became critical for managing cost and accelerating query performance as data moved to cloud data platforms like Snowflake, Databricks, and Google BigQuery. With the advent of generative AI, we’re finding yet another value proposition for a universal semantic layer. By providing a contextual layer on top of the data platform, the semantic layer can inform LLMs of the correct business logic, metrics, and relationships, eliminating the need to infer these elements from scratch.
The semantic layer standardizes business metrics and logic and provides additional metadata that LLMs can leverage during NLQ tasks. This includes business-relevant names, warehouse names, and descriptions critical for translating natural language into accurate SQL queries.
Generative Inconsistencies: While LLMs are powerful at generating responses, they can produce inconsistent or incorrect results when handling calculations and KPIs.
Terminology Mismatch: Database tables and columns are often named according to data engineering conventions, which may differ from the business terminology used in queries. With context, LLMs france whatsapp number data find it easier to map between these different terminologies.
Addressing Challenges with a Semantic Layer
The primary use case for a universal semantic layer is to allow enterprises to democratize data access with accuracy, safety, and consistency. While this initial value proposition is compelling enough on its own, the semantic layer also became critical for managing cost and accelerating query performance as data moved to cloud data platforms like Snowflake, Databricks, and Google BigQuery. With the advent of generative AI, we’re finding yet another value proposition for a universal semantic layer. By providing a contextual layer on top of the data platform, the semantic layer can inform LLMs of the correct business logic, metrics, and relationships, eliminating the need to infer these elements from scratch.
The semantic layer standardizes business metrics and logic and provides additional metadata that LLMs can leverage during NLQ tasks. This includes business-relevant names, warehouse names, and descriptions critical for translating natural language into accurate SQL queries.