LLM Evaluation for Analytics Use Cases: A Practical Framework

You've trained a text-to-SQL model. It converts natural language into queries. You've integrated it into your analytics platform. Now what? How do you know it actually works in production? How do you measure drift, catch hallucinations, and prove to stakeholders that the model is safe to ship?

This is the problem most data and engineering teams face when building LLM-powered analytics features. The stakes are high—a bad SQL query doesn't just waste compute; it can serve incorrect metrics to decision-makers, erode trust in your platform, and create compliance headaches.

This guide walks you through building a practical evaluation suite for analytics-specific LLM use cases. We'll cover the frameworks, metrics, and testing strategies that work at scale. Whether you're embedding AI-powered analytics into your product or building internal dashboards with text-to-SQL capabilities, this framework applies.

Why Standard LLM Evals Don't Cut It for Analytics

When you search for LLM evaluation best practices, you'll find plenty of guidance on general-purpose models. But analytics use cases are fundamentally different from chatbots or summarization tasks.

In a general LLM application, "good" output is often subjective. A summary can be helpful in multiple ways. A translation can be accurate yet creative. But in analytics, there's a ground truth. A SQL query either returns the correct result set or it doesn't. A metric calculation is either accurate or it's misleading your entire organization.

This binary nature of correctness creates unique evaluation challenges. You can't rely on human raters scoring outputs on a 1-5 scale. You need deterministic, repeatable tests that validate both the query structure and the results it produces.

Additionally, analytics LLM use cases often involve multi-step processes. Text-to-SQL is just the first step. The model might need to:

• Parse a natural language question about your data
• Understand your database schema and business logic
• Generate syntactically correct SQL for your specific dialect (PostgreSQL, Snowflake, BigQuery, etc.)
• Execute the query without errors
• Format and contextualize the results for end users
• Handle edge cases like null values, time zones, and aggregation logic

Evaluating each step independently—and then evaluating the entire pipeline—requires a different framework than general LLM evaluation.

Understanding the Two Evaluation Paradigms

When building evaluation suites for analytics LLMs, you're working with two complementary evaluation approaches: property evals and correctness evals. Understanding when to use each is critical.

Property Evals test whether your LLM output has certain characteristics, independent of a ground-truth answer. These include:

• Syntactic correctness: Does the generated SQL parse? Is it valid for your database dialect?
• Schema adherence: Does the query reference tables and columns that actually exist in your schema?
• Safety properties: Does the query avoid dangerous operations (dropping tables, modifying data, accessing restricted columns)?
• Efficiency heuristics: Is the query likely to be performant (avoiding full table scans, excessive joins, etc.)?
• Consistency: Does the model generate the same query for the same question when run multiple times?

Property evals are fast, deterministic, and don't require a ground-truth dataset. You can run them on every model output in production. They catch obvious mistakes—malformed SQL, schema violations, dangerous operations—before queries ever execute.

Correctness Evals compare the model's output against a known correct answer. These include:

• Result correctness: Does the query return the exact same result set as a manually-verified reference query?
• Semantic correctness: Does the query answer the user's question accurately, even if the SQL differs from the reference?
• Metric accuracy: For specific KPI or metric queries, is the calculated value correct within acceptable tolerance?
• Edge case handling: Does the query correctly handle nulls, duplicates, time zone conversions, and other edge cases?

Correctness evals require building a test dataset with known answers. This is more work upfront, but it's the only way to truly validate that your model produces accurate results.

According to practical frameworks for LLM system evaluations, the key question when planning your eval suite is: "Which properties matter for my use case, and which require ground truth?"

For analytics, the answer is: you need both. Property evals catch systemic failures; correctness evals catch subtle semantic errors.

Building Your Evaluation Dataset

Your evaluation dataset is the foundation of your eval suite. It needs to be representative, comprehensive, and maintainable.

Defining Your Test Coverage

Start by categorizing the types of questions your users will ask:

• Simple aggregations: "What was total revenue last month?"
• Filtered queries: "How many active users do we have in California?"
• Multi-table joins: "What's the average order value by customer segment?"
• Time-based analysis: "Show me month-over-month growth in signups."
• Complex logic: "Calculate customer lifetime value for cohorts acquired in Q3."
• Edge cases: "What's the average for metrics with no data?"

For each category, you need test cases that cover:

1. Happy path: Standard questions your users ask every day
2. Boundary conditions: Questions at the edges of your data (oldest dates, largest values, etc.)
3. Ambiguous intent: Questions that could be interpreted multiple ways
4. Null handling: Questions where the answer might be zero or null
5. Schema changes: Questions that still work after you add/rename columns

Creating Reference Answers

For each test case, you need a reference answer. This is the ground truth against which you'll measure your model.

For SQL evaluation, the reference answer is a manually-written SQL query that you've verified by running it against your database and inspecting the results. Have a domain expert (ideally someone who knows your data well) write the reference query. Have a second person review it. Document assumptions and edge cases.

Store these reference answers in version control alongside your test cases. As your schema evolves, update both the test cases and reference answers.

Sizing Your Dataset

How many test cases do you need? There's no magic number, but consider:

• Minimum: 50-100 test cases covering the major categories above
• Recommended: 200-500 test cases for production analytics platforms
• Comprehensive: 1000+ test cases if you're evaluating across multiple data domains or database dialects

Start small (50 cases), run your eval suite, identify gaps, and expand. You'll likely find that 80% of your test cases cover 20% of the failure modes, and you'll keep adding edge cases as you discover them in production.

Implementing Property Evaluations

Property evals are your first line of defense. They're fast, deterministic, and catch obvious mistakes.

Syntactic Validation

The simplest property eval: does the generated SQL parse?

import sqlparse
 
def eval_syntax(generated_sql: str) -> dict:
    try:
        parsed = sqlparse.parse(generated_sql)
        if not parsed or not parsed[0].tokens:
            return {"passed": False, "reason": "Empty or invalid parse"}
        return {"passed": True, "reason": "Valid SQL syntax"}
    except Exception as e:
        return {"passed": False, "reason": f"Parse error: {str(e)}"}

This catches typos, incomplete queries, and malformed SQL before it ever reaches your database.

Schema Validation

Does the query reference tables and columns that exist in your schema?

def eval_schema_adherence(generated_sql: str, schema: dict) -> dict:
    """Validate that query references only existing tables/columns."""
    parsed = sqlparse.parse(generated_sql)[0]
    
    # Extract table and column references
    referenced_tables = extract_tables(parsed)
    referenced_columns = extract_columns(parsed)
    
    valid_tables = set(schema.keys())
    valid_columns = {col for table in schema.values() for col in table['columns']}
    
    invalid_tables = referenced_tables - valid_tables
    invalid_columns = referenced_columns - valid_columns
    
    if invalid_tables or invalid_columns:
        return {
            "passed": False,
            "invalid_tables": list(invalid_tables),
            "invalid_columns": list(invalid_columns)
        }
    return {"passed": True}

This prevents queries that reference columns you've renamed or tables that don't exist.

Safety Checks

Does the query contain dangerous operations?

DANGEROUS_KEYWORDS = {
    'DROP', 'DELETE', 'TRUNCATE', 'ALTER', 'CREATE', 
    'INSERT', 'UPDATE', 'GRANT', 'REVOKE'
}
 
def eval_query_safety(generated_sql: str) -> dict:
    """Ensure query doesn't contain write operations or dangerous commands."""
    tokens = sqlparse.parse(generated_sql)[0].tokens
    dangerous_found = []
    
    for token in tokens:
        if token.ttype is sqlparse.tokens.Keyword and token.value.upper() in DANGEROUS_KEYWORDS:
            dangerous_found.append(token.value)
    
    if dangerous_found:
        return {"passed": False, "dangerous_keywords": dangerous_found}
    return {"passed": True}

This is a hard boundary—no write operations, ever, in a read-only analytics context.

Performance Heuristics

Will the query be efficient?

def eval_query_efficiency(generated_sql: str, schema: dict) -> dict:
    """Check for common performance anti-patterns."""
    warnings = []
    parsed = sqlparse.parse(generated_sql)[0]
    
    # Check for full table scans (no WHERE clause)
    if 'WHERE' not in generated_sql.upper():
        largest_table = max(schema.values(), key=lambda t: t.get('row_count', 0))
        if largest_table.get('row_count', 0) > 1_000_000:
            warnings.append(f"Large table scan without WHERE clause")
    
    # Check for expensive joins
    join_count = generated_sql.upper().count('JOIN')
    if join_count > 3:
        warnings.append(f"Multiple joins ({join_count}) may be inefficient")
    
    # Check for functions on indexed columns
    if 'LOWER(' in generated_sql.upper() or 'UPPER(' in generated_sql.upper():
        warnings.append("Functions on columns may prevent index usage")
    
    return {
        "passed": len(warnings) == 0,
        "warnings": warnings
    }

These heuristics aren't foolproof—sometimes a full table scan is correct—but they catch obvious inefficiencies.

Implementing Correctness Evaluations

Property evals catch structural problems. Correctness evals catch semantic errors—queries that parse and execute but return wrong answers.

Result Set Comparison

The most direct correctness eval: does the model's query return the same results as the reference query?

def eval_result_correctness(
    generated_sql: str,
    reference_sql: str,
    db_connection
) -> dict:
    """Execute both queries and compare result sets."""
    try:
        generated_results = pd.read_sql(generated_sql, db_connection)
        reference_results = pd.read_sql(reference_sql, db_connection)
    except Exception as e:
        return {"passed": False, "reason": f"Execution error: {str(e)}"}
    
    # Normalize column order and names
    generated_results.columns = [c.lower() for c in generated_results.columns]
    reference_results.columns = [c.lower() for c in reference_results.columns]
    
    # Sort by all columns for deterministic comparison
    generated_results = generated_results.sort_values(by=list(generated_results.columns)).reset_index(drop=True)
    reference_results = reference_results.sort_values(by=list(reference_results.columns)).reset_index(drop=True)
    
    # Compare
    if generated_results.equals(reference_results):
        return {"passed": True}
    else:
        return {
            "passed": False,
            "reason": "Result sets differ",
            "generated_shape": generated_results.shape,
            "reference_shape": reference_results.shape,
            "sample_diff": identify_differences(generated_results, reference_results)
        }

This is binary: either the results match or they don't. It's the gold standard for correctness.

Metric Accuracy Evaluation

For KPI queries, you might accept small numerical differences (due to rounding, floating-point precision, etc.).

def eval_metric_accuracy(
    generated_sql: str,
    reference_sql: str,
    db_connection,
    tolerance: float = 0.01  # 1% tolerance
) -> dict:
    """Compare metric values with tolerance for numerical differences."""
    try:
        generated_result = pd.read_sql(generated_sql, db_connection)
        reference_result = pd.read_sql(reference_sql, db_connection)
    except Exception as e:
        return {"passed": False, "reason": f"Execution error: {str(e)}"}
    
    # Extract metric values (assume single row, single column)
    if len(generated_result) != 1 or len(reference_result) != 1:
        return {"passed": False, "reason": "Expected single-row results"}
    
    gen_value = float(generated_result.iloc[0, 0])
    ref_value = float(reference_result.iloc[0, 0])
    
    # Handle zero values
    if ref_value == 0:
        if gen_value == 0:
            return {"passed": True}
        else:
            return {"passed": False, "reason": f"Expected 0, got {gen_value}"}
    
    # Calculate percentage difference
    pct_diff = abs(gen_value - ref_value) / abs(ref_value)
    
    if pct_diff <= tolerance:
        return {"passed": True, "pct_diff": pct_diff}
    else:
        return {
            "passed": False,
            "generated_value": gen_value,
            "reference_value": ref_value,
            "pct_diff": pct_diff,
            "tolerance": tolerance
        }

This allows for acceptable numerical variance while catching major errors.

Semantic Correctness (The Hard Problem)

Sometimes two different queries can both be correct answers to the same question. For example:

• Question: "What's the average order value?"
• Reference: SELECT AVG(order_total) FROM orders
• Generated: SELECT SUM(order_total) / COUNT(*) FROM orders

Both queries return the same result, but the SQL is different. Evaluating semantic correctness requires either:

1. Execution equivalence: Run both queries and compare results (covered above)
2. LLM-based evaluation: Use another LLM to judge if the generated query correctly answers the question
3. Expert review: Have a human verify that the generated query is semantically correct

For production systems, execution equivalence is most reliable. If two queries return the same results, they're semantically equivalent (for that dataset).

Scaling Evaluation: Frameworks and Tools

As your analytics platform grows, manual evaluation becomes unsustainable. You need automated frameworks.

Several open-source and commercial evaluation frameworks exist. According to comprehensive comparisons of LLM evaluation frameworks, the most relevant for analytics use cases include:

RAGAS (Retrieval-Augmented Generation Assessment) was originally designed for RAG pipelines, but its metrics translate well to analytics. It evaluates:

• Context relevance: Does the generated query select relevant data?
• Faithfulness: Does the result accurately reflect the underlying data?
• Answer relevance: Does the result answer the user's question?

While RAGAS focuses on text output, you can adapt its evaluation approach to SQL generation.

TruLens provides a framework for evaluating LLM applications end-to-end. For analytics, you'd use TruLens to:

• Instrument your text-to-SQL pipeline
• Evaluate each step (parsing, SQL generation, execution)
• Track evaluation results over time
• Identify drift in model performance

DeepEval offers a lightweight evaluation framework that integrates with your CI/CD pipeline. You can define custom metrics for your analytics use case and run them automatically on every model update.

Beyond open-source tools, platforms like Humanloop and Arize provide enterprise-grade evaluation infrastructure with monitoring, alerting, and version control for your evaluation datasets.

Real-World Example: Text-to-SQL Evaluation Pipeline

Let's walk through a concrete example: evaluating a text-to-SQL model for a SaaS analytics platform.

Setup

Your platform has:

• A PostgreSQL database with 50+ tables
• 500+ daily active users asking questions in natural language
• A fine-tuned LLM that generates SQL from questions
• An existing embedded analytics platform where users explore data

Evaluation Dataset

You create 250 test cases:

1. 50 simple queries: Single-table aggregations, filtering
2. 50 join queries: Multi-table aggregations
3. 50 time-series queries: Month-over-month, year-over-year, trend analysis
4. 50 complex queries: Subqueries, CTEs, window functions
5. 50 edge cases: Null handling, division by zero, empty result sets

For each test case, you have:

• A natural language question ("What's our MRR for customers acquired in Q3?")
• A reference SQL query (manually written and verified)
• Expected results (the correct answer)

Evaluation Pipeline

Your CI/CD pipeline runs:

1. Property Evals (instant, before execution):

   • Syntax validation
   • Schema adherence
   • Safety checks
   • Performance heuristics

2. Correctness Evals (on a test database):

   • Result set comparison
   • Metric accuracy (with 0.1% tolerance for financial metrics)
   • Edge case handling

3. Regression Testing:

   • Run all 250 test cases against the new model
   • Compare pass rates to the previous version
   • Flag any regressions

4. Monitoring:

   • Track eval results over time
   • Alert if pass rate drops below 95%
   • Log failures for manual review

Results

Your evaluation shows:

• 99% property evals pass: Syntax, schema, and safety checks catch 99% of obvious errors
• 94% correctness evals pass: The model generates correct SQL for 94% of test cases
• Failure modes: Most failures are in complex joins (5% failure rate) and edge cases with null values (3% failure rate)

You use these insights to:

• Add more training examples for complex joins
• Implement special handling for null values in your prompt
• Create a "confidence score" that flags queries where the model is uncertain

Advanced: Multi-Step Evaluation for Summarization

Text-to-SQL is just one analytics use case. Many platforms also use LLMs to summarize query results, generate insights, or explain trends.

Evaluating summarization is harder than evaluating SQL—there's more subjectivity in what makes a "good" summary.

However, you can still implement deterministic evaluations:

Factual Accuracy

Does the summary accurately reflect the data?

def eval_summary_accuracy(
    summary: str,
    query_results: pd.DataFrame,
    reference_summary: str
) -> dict:
    """Check if summary contains factually correct claims about the data."""
    # Extract numeric claims from summary
    claims = extract_numeric_claims(summary)
    
    # Verify each claim against the data
    verified_claims = []
    for claim in claims:
        metric, expected_value = parse_claim(claim)
        actual_value = extract_metric_from_results(metric, query_results)
        
        if is_approximately_equal(actual_value, expected_value, tolerance=0.05):
            verified_claims.append({"claim": claim, "verified": True})
        else:
            verified_claims.append({"claim": claim, "verified": False, "actual": actual_value})
    
    accuracy = sum(1 for c in verified_claims if c["verified"]) / len(verified_claims)
    
    return {
        "passed": accuracy > 0.9,  # 90% of claims must be accurate
        "accuracy": accuracy,
        "failed_claims": [c for c in verified_claims if not c["verified"]]
    }

Completeness

Does the summary cover the key insights in the data?

def eval_summary_completeness(
    summary: str,
    query_results: pd.DataFrame,
    key_metrics: list
) -> dict:
    """Check if summary mentions important metrics and trends."""
    mentioned_metrics = []
    missing_metrics = []
    
    for metric in key_metrics:
        if metric_mentioned_in_summary(metric, summary):
            mentioned_metrics.append(metric)
        else:
            missing_metrics.append(metric)
    
    coverage = len(mentioned_metrics) / len(key_metrics)
    
    return {
        "passed": coverage > 0.8,  # 80% of key metrics mentioned
        "coverage": coverage,
        "mentioned": mentioned_metrics,
        "missing": missing_metrics
    }

Tone and Clarity

For subjective qualities like tone and clarity, you might use another LLM as a judge:

async def eval_summary_quality_with_llm(
    summary: str,
    query_results: pd.DataFrame,
    llm_client
) -> dict:
    """Use an LLM to evaluate summary quality on subjective dimensions."""
    prompt = f"""
    Evaluate this summary of analytics data on the following dimensions:
    1. Clarity: Is the summary easy to understand?
    2. Actionability: Does it suggest what to do with the insights?
    3. Tone: Is the tone appropriate for a business audience?
    
    Summary: {summary}
    
    Data: {query_results.to_string()}
    
    Respond in JSON format with scores 1-5 for each dimension.
    """
    
    response = await llm_client.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    
    scores = json.loads(response.choices[0].message.content)
    
    return {
        "passed": all(score >= 4 for score in scores.values()),
        "scores": scores
    }

This approach is less deterministic than SQL evaluation, but it captures important quality dimensions.

Handling Drift and Continuous Improvement

Once your eval suite is in place, you need to monitor it continuously. Model performance degrades over time due to:

• Data drift: Your schema changes, tables get renamed, new columns appear
• Distribution shift: Users ask different types of questions than your training data
• Model drift: The underlying LLM is updated, or fine-tuning data changes

Monitoring Evaluation Results

Track these metrics over time:

• Pass rate: Percentage of test cases that pass
• Failure breakdown: Which categories have the most failures?
• Latency: How long does evaluation take?
• Regression rate: How many previously-passing tests now fail?

def track_eval_metrics(eval_results: list, timestamp: datetime) -> dict:
    """Aggregate evaluation results for monitoring."""
    total = len(eval_results)
    passed = sum(1 for r in eval_results if r['passed'])
    
    failures_by_category = {}
    for result in eval_results:
        if not result['passed']:
            category = result.get('category', 'unknown')
            failures_by_category[category] = failures_by_category.get(category, 0) + 1
    
    return {
        "timestamp": timestamp,
        "total_tests": total,
        "passed": passed,
        "pass_rate": passed / total,
        "failures_by_category": failures_by_category,
        "needs_investigation": passed / total < 0.95
    }

Alerting and Response

Set up alerts for:

• Pass rate drops below 95%: Investigate immediately
• New failure pattern: A category that previously passed now fails
• Latency spike: Evaluation takes significantly longer
• Execution errors: Database queries fail consistently

When an alert fires, your process should be:

1. Check if there's a recent model update
2. Check if there's a recent schema change
3. Run evaluation on the previous model version to confirm it's a regression
4. If it's a regression, roll back the model or schema change
5. If it's a new issue, add it to your backlog for investigation

Expanding Your Eval Suite

As you discover failures in production, add them to your eval suite:

1. A user asks a question that the model fails on
2. You manually write the correct SQL for that question
3. You add both the question and the reference SQL to your test dataset
4. You run evaluation to confirm the model fails
5. You fix the model (retraining, prompt engineering, etc.)
6. You confirm the test now passes
7. The test becomes part of your permanent eval suite, preventing regression

This creates a virtuous cycle: each production failure becomes a permanent test that prevents future regressions.

Integration with Your Analytics Platform

If you're building analytics features on managed Apache Superset, evaluation frameworks integrate naturally:

• Superset's API allows you to programmatically create and test dashboards
• You can generate test queries and validate them against Superset's query engine
• Results can be exported for evaluation
• Evaluation metrics can be logged and monitored over time

For teams building self-serve BI platforms with text-to-SQL capabilities, the evaluation framework becomes part of your platform's quality gates. Every model update is gated on eval suite pass rate before deployment.

Best Practices and Lessons Learned

Based on practical frameworks for LLM system evaluations and industry experience:

Start with Property Evals

Property evals are fast and deterministic. Implement syntax, schema, and safety checks first. They catch 80% of failures with 5% of the effort.

Build Your Correctness Eval Dataset Incrementally

Don't try to create 500 test cases upfront. Start with 50, run evaluation, identify gaps, and expand. You'll learn what matters as you go.

Version Your Evaluation Dataset

Treat your eval dataset like code. Version control it. Document changes. When your schema evolves, update your test cases and reference answers together.

Separate Training and Evaluation Data

Your model shouldn't see your eval test cases during training. Otherwise, you're not measuring generalization.

Monitor Eval Metrics in Production

Evaluation shouldn't stop at deployment. Track eval results for every query in production. Use this data to identify drift and trigger retraining.

Make Evaluation Part of Your Definition of Done

A feature isn't done until it passes your eval suite. Eval suite pass rate is a key metric for model quality.

Invest in Tooling

Evaluation at scale requires tooling. Use frameworks like TruLens, RAGAS, or DeepEval instead of building from scratch. These tools handle versioning, monitoring, and reporting.

Conclusion

Evaluating LLMs for analytics is fundamentally different from evaluating general-purpose models. The stakes are higher—incorrect metrics erode trust in your platform. The correctness requirements are stricter—there's often a ground truth. And the use cases are more complex—multi-step pipelines with multiple failure modes.

But this also means evaluation is more tractable. You can define deterministic tests. You can build datasets with known answers. You can measure progress objectively.

The framework outlined here—property evals for structural correctness, correctness evals for semantic accuracy, and continuous monitoring for drift—is battle-tested in production analytics platforms. It catches failures before they reach users. It gives you confidence to ship new models. And it creates a feedback loop that continuously improves your system.

Start with a small eval suite (50-100 test cases). Implement property evals first, then add correctness evals. Integrate evaluation into your CI/CD pipeline. Monitor results in production. Expand your test dataset as you discover edge cases. Over time, you'll build a robust quality gate that keeps your analytics platform reliable and trustworthy.

For teams building on managed platforms like D23, evaluation frameworks integrate seamlessly with your analytics infrastructure. Whether you're evaluating text-to-SQL models, building embedded analytics, or implementing self-serve BI with AI assistance, the principles in this guide apply. Evaluation is how you ensure that AI-powered analytics deliver accurate insights, not just plausible-sounding ones.

The investment in building a comprehensive evaluation suite pays dividends in confidence, reliability, and user trust. It's the difference between shipping an LLM feature and shipping one you're proud to stand behind.