Elevating SQL accuracy for retail analytics at scale

The Challenge

A major Fortune-50 retailer implemented RAG-based tools with in-house SQL Agents to convert natural language queries into SQL. The system faced critical issues:

• Slow execution - Wrong queries and poor scaling made it difficult for non-technical teams to use
• Schema mismatches - SQL syntax errors and schema mismatches affected query accuracy
• Poor context awareness - Agents struggled with table relationships, returning incomplete data
• BI limitations - Teams confined to rigid Tableau/Power BI dashboards with predefined filters

Non-technical teams relied on BI dashboards, but rigid schemas restricted flexible data exploration and creative analysis.

The Solution

Future AGI deployed a three-phase evaluation methodology to validate SQL agents:

Phase 1: Deterministic Evaluation

SQL structure validation comparing table layout, user intent, and generated SQL for syntactic correctness.

Phase 2: Context Sufficiency

Pre-execution context verification ensuring the SQL agent had sufficient table context to generate accurate queries.

Phase 3: Completeness Evaluation

Answer accuracy assessment measuring output against expected results-verifying the query returned the right data for the question asked.

Technical Implementation

pip install futureagi

Key testing components included deterministic checks, context sufficiency scoring, and completeness assessment measuring output against input questions.

The Results

• 10x faster SQL query validation
• 90% reduction in query errors
• 5x decrease in repeated queries
• Enhanced scalability handling large query volumes
• Improved user trust driving broader adoption of self-service analytics

Key Findings

• Flawed SQL structures caused failures from wrong columns and missing conditions
• Incomplete answers lacked critical information that users needed
• Weak context from limited table references reduced clarity
• Systematic evaluation caught issues that manual review missed