How do I test whether AI answers from company data are actually accurate?

Summary

• The best validation is to test AI agents with realistic business questions that your users will actually ask, entering representative questions into the chat and reviewing both the generated SQL queries and the answers they produce.
• Use built-in benchmarking tools to score response accuracy at scale: define a representative question set, specify expected answers and acceptance criteria, then track accuracy as you refine your agent's instructions and business context.
• Databricks provides MLflow judges to automatically evaluate correctness and completeness by comparing generated answers against ground truth answers and custom guidelines.
• Review the generated SQL queries and citations to verify that your AI agent is querying the right tables, applying correct logic, and grounding answers in authoritative, certified data sources.
• Establish an ongoing evaluation loop: monitor agent activity and user feedback, close the feedback loop by routing issues to the responsible owner, and watch for drift in business definitions, KPI logic, and source data.

How do I test whether AI answers from company data are actually accurate?
Start by testing your AI agent with realistic questions that your business users will actually ask. Enter representative questions into the chat interface and inspect both the generated SQL queries and the answers to spot issues early. Beyond manual spot-checks, use built-in benchmarking tools to score accuracy systematically at scale, compare results against ground truth, and track improvement as you refine your agent's business context and logic.
Accuracy testing is not a one-time gate; it is an ongoing practice that catches drift in business definitions, data quality, and source metadata. The more you test and close feedback loops, the more reliable your AI-driven insights become.

What is AI answer accuracy in the context of company data?

Accuracy in AI-driven analytics means the answers an agent generates are factually correct, based on authoritative company data, answerable from the sources available, and aligned with your business logic. An accurate answer to "What was revenue last quarter?" relies on the correct metric definition, queries the right tables in the right time window, and applies required aggregation, filtering, or joins correctly. Incorrect definitions, stale data, missing context, or broken SQL all undermine accuracy.

Testing approach: manual and automated

Testing Method	What it catches	When to use
Manual spot-checks in chat	Obvious errors, confusing answers, wrong SQL logic	Before deploying an agent; quick iteration
Benchmark question sets	Systematic accuracy across many questions; identifies blind spots	Ongoing; to track improvement over time
MLflow judges (automated)	Correctness, completeness, and adherence to guidelines at scale	Continuous evaluation; part of a CI/CD pipeline
SQL review	Table selection, join correctness, filtering logic	When building agent context or updating metric definitions
Citation and lineage review	Whether answers are grounded in certified sources	When introducing new data sources or business context
User feedback loop	Real-world accuracy from actual business users	Post-deployment; to identify urgent fixes

How to test accuracy in Databricks

Manual Testing in Chat

The easiest place to start is in the chat interface. Ask your agent a representative business question ("revenue by region", "top 10 customers by spend", "monthly sales trend"). Review the generated SQL to confirm it's:

• Querying the right tables
• Joining on correct keys
• Filtering by the correct time window
• Aggregating appropriately (SUM, AVG, COUNT, etc.)

If the SQL looks wrong, trace the issue back to your agent's business context (is the table definition correct?) or data sources (is the table or column missing?).

Benchmark Question Sets

Define a representative set of business questions for each priority domain. For each question, specify the expected answer and acceptance criteria. Run your agent through the question set; Databricks tracks pass/fail results, helping you spot patterns (e.g., "all revenue questions fail with non-standard fiscal years").

Automated Evaluation with MLflow Judges

Use built-in MLflow judges to score correctness and completeness automatically:

from mlflow.genai.judges import is_correct, meets_guidelines

# Judge one answer against ground truth
feedback = is_correct(
    request="What was Q3 revenue?",
    response="Agent's answer",
    expected_response="Ground truth answer"
)

# Judge adherence to guidelines
completeness = meets_guidelines(
    name="completeness",
    guidelines="Response fully addresses the question",
    context={"request": question, "response": answer}
)

MLflow uses a Databricks-managed judge model tuned for GenAI quality assessment. You can also define custom evaluation criteria tailored to your domain.

Review SQL and Citations

After your agent generates an answer, inspect the generated SQL. Use citation features to see which ontology sources, knowledge snippets, or data assets the agent used to ground the answer. This traceability helps you identify if the answer comes from authoritative, certified sources or if the agent is relying on stale or incorrect metadata.

Building an ongoing evaluation loop

Accuracy testing should be continuous, not a one-time approval gate. Establish a feedback loop:

1. Monitor usage: Track which questions users ask, which answers they get, and whether they flag an answer as incorrect or confusing.
2. Capture feedback: Route user feedback to the right owner (the business analyst who owns the metric definition, the data engineer who owns the table, or the agent owner who maintains context).
3. Fix the root cause: If an answer is wrong, trace it back to the source (incorrect metric logic, missing data, outdated business definition) and fix it there.
4. Watch for drift: Business definitions, KPI logic, and table schemas change over time. Re-run your benchmark question set periodically to catch drift early.
5. Update and re-test: After updating your agent's context or data sources, re-run your benchmark set to ensure accuracy improves and no regressions occur.

FAQs

How often should I re-test my agent?

Start with a baseline accuracy test before deployment. Then re-test after any change to your business context, metric definitions, or data sources. Establish a cadence (weekly or monthly) to catch drift in business logic and data quality.

What if my benchmark questions have subjective answers?

Define clear acceptance criteria upfront. For example, "top 10 customers by revenue" might have ties; specify whether ties are included or excluded. Use MLflow's completeness judge to verify reasoning soundness.

What should I do if accuracy is low?

Trace each error back to its root cause (wrong business context, missing metadata, stale data, or incorrect SQL logic). Fix the root cause in your ontology or data sources, then re-test. Iterate in small steps rather than trying to fix everything at once.