1. Introduction

SQL Query Validation sits at the heart of every successful data workflow. A Fortune-50 leader in Retail Analytics discovered this truth the hard way when its RAG-based tool powered by in-house SQL Agents that translate Natural language to SQL slowed inventory checks and customer-insight reports. Slow execution, wrong queries, and bad scaling made it hard for people to use. After Future AGI took over, the quality of queries went up, stability came back, and user trust grew, all without having to rewrite the existing stack.

2. Why Bridging SQL Complexity and Business Needs Matters

You need to know a lot about SQL to write it. So, non-technical teams use BI dashboards like Tableau or Power BI. But those layers add three problems:

• Rigid, set schemas limit flexible exploration.

• Creative analysis is limited by a lack of filters and metrics.

• Limited discovery keeps people stuck in charts that were already made.

Emerging SQL Agents can turn plain language into exact SQL, but there are still three risks:

1. The schema and syntax must match the accuracy of the query structure.

2. Context awareness has to show real table relationships.

3. Output precision must return full, correct data.

3. How Future AGI’s AI-Driven Evaluation Framework Works

Future AGI injected an optimization loop that provides:

• Enhanced query validation for syntactic and contextual fitness.

• Advanced NLP refinement that tunes prompts to live database layouts.

• Scalable performance boosts that wipe out execution bottlenecks.

4. Evaluation Methodology: What Three-Phase Testing Proves 

1. Validate SQL structure with Deterministic Evaluation.

2. Check context sufficiency before running a query.

3. Measure answer accuracy against expected results.

4.1 Installing Future AGI and Starting the Evaluation Client 

pip install futureagi

from fi.evals import EvalClient

evaluator = EvalClient(fi_api_key=FI_API_KEY,

                      fi_secret_key=FI_SECRET_KEY,

                      fi_base_url="<https://api.futureagi.com>")

4.2 Loading the Dataset and Running Deterministic Eval 

import pandas as pd
dataset = pd.read_csv("data.csv")

The Deterministic check assigns Pass or Fail by comparing table layout, user intent, and generated SQL:

from fi.testcases import MLLMTestCase

from fi.evals import Deterministic

class DeterministicTestCase(MLLMTestCase):

   table: str

   question: str

   sql: str

  

deterministic_eval = Deterministic(config={

 "multi_choice": False,

 "choices": ["Pass", "Fail"],

 "rule_prompt": '''table : {{input_key1}}, question : {{input_key2}}, sql : {{input_key3}}.

                   Given the table, question and sql, choose Pass if the sql is according to the question from table, else choose Fail''',

     "input": {

     "input_key1": "table",

     "input_key2": "question",

     "input_key3": "sql"

     }

})

complete_result = {}

options = []

reasons = []

for index, row in dataset.iterrows():

 test_case = DeterministicTestCase(

     table=row["table"],

     question=row["question"],

     sql=row["sql"]

     )

 result = evaluator.evaluate([deterministic_eval], [test_case])

 option = result.eval_results[0].data[0]

 reason = result.eval_results[0].reason

 options.append(option)

 reasons.append(reason)

complete_result["Det-Eval-Rating"] = options

complete_result["Det-Eval-Reason"] = reasons

4.3 Result After Validating SQL Queries Using Deterministic Eval:

4.4 Evaluating Context Sufficiency for Each SQL Query 

from fi.testcases import TestCase

from fi.evals.templates import ContextSufficiency

context_scores = []

context_reasons = []

for _, row in dataset.iterrows():

   test_case = TestCase(

       query=row["sql"],

       context=row["table"]

   )

   context_template = ContextSufficiency(config={

       "model": "gpt-4o-mini"

   })

   response = evaluator.evaluate(eval_templates=[context_template], inputs=[test_case])

   context_result = response.eval_results[0].metrics[0].value

   reason = response.eval_results[0].reason

   context_scores.append(context_result)

   context_reasons.append(reason)

dataset["context_sufficiency_score"] = context_scores

dataset["context_sufficiency_reason"] = context_reasons

complete_result["Context-Eval-Score"] = context_scores

complete_result["Context-Eval-Reason"] = context_reasons

4.5 Result After Evaluating Context Sufficiency for SQL Queries:

4.6 Assessing SQL Agent Accuracy (Completeness Eval) 

from fi.testcases import TestCase

from fi.evals.templates import Completeness

completeness_scores = []

completeness_reasons = []

for _, row in dataset.iterrows():

   test_case = TestCase(

       input=row["question"],

       output=row["output"]

   )

   completeness_template = Completeness(config={

       "required_keys": ["input", "output"],

       "output": "completeness_score",

   })

   response = evaluator.evaluate(eval_templates=[completeness_template], inputs=[test_case])

   completeness_result = response.eval_results[0].metrics[0].value

   reason = response.eval_results[0].reason

   completeness_scores.append(completeness_result)

   completeness_reasons.append(reason)

dataset["completeness_score"] = completeness_scores

dataset["completeness_reason"] = completeness_reasons

complete_result["Completeness-Eval-Score"] = completeness_scores

complete_result["Completeness-Eval-Reason"] = completeness_reasons

4.7 Result After Evaluating SQL Agent Accuracy in Answering Queries:

5. Evaluate Your RAG Tool in Future AGI’s Dashboard 

Besides the Python SDK, analysts can drag-and-drop datasets into a no-code interface. Graphs reveal validation status, context sufficiency, and completeness, so teams iterate quickly.

6. Key Findings 

• Flawed SQL structures: Wrong columns or missing conditions caused fails.

• Incomplete answers: Some questions left out important information, which made the answers less clear.

• Weak context: Limited tables made the results unclear.

Systematic checks:

1. Optimise SQL agents to cut down on structural errors.

2. Make sure that retrieval is reliable for better BI and automation.

3. Increase trust by cutting down on bad results.

Impact 

• Every SQL query statement is checked 10 times faster.

• 90 % fewer query errors, thanks to deterministic and context checks.

• Enhanced scalability, handling large query volumes without lag.

• 5× drop in repeat queries, proving consistent accuracy.

• Higher trust in RAG-powered analytics, driving broader usage.

  
  

Conclusion 

Future AGI makes large-scale retail analytics more accurate by turning natural language into reliable SQL for SQL Query Validation. The company now moves inventory data and customer insights quickly by fixing problems and building trust. One stakeholder said it best: "Without these upgrades, our analytics would still hit walls."