Use this when you want a model to answer questions about tables, metrics, dashboards, or operational systems without making things up.
The short answer: if the answer should come from a database, API, or validated report, ground the model in that source. Do not ask it to “know” the answer from memory or loosely related context.
What you will get in 10 minutes
- A clear distinction between grounded QA and generic document QA
- When to use SQL, tool calls, or retrieval
- A simple grounding decision tree
- Metrics that catch fabricated answers early
Use this when
- Users ask questions about structured records or metrics
- The system needs to answer from live operational data
- The cost of a fabricated answer is high
- You are tempted to treat every QA use case as RAG
The 60-second answer
| Data source | Best pattern |
| --- | --- |
| Database tables | SQL or tool-based grounding |
| APIs and business systems | function or tool calling |
| Reports and docs | retrieval with citations |
| Mixed systems | router plus grounded answer path |
If the answer belongs to a system of record, route through that system of record.
Grounded QA is not just “better prompting”
Grounded QA means the answer is constrained by evidence outside the model.
That usually means one of:
- SQL query generation plus validation
- tool or API call plus answer synthesis
- retrieval over approved reports or documents
The model can still help with interpretation, but it should not invent facts the system could have retrieved directly.
Pattern 1: SQL or structured query path
Use when the answer comes from a warehouse, analytics table, or operational database.
Best for:
- “What was spend by provider last week?”
- “Which customers churned after plan downgrade?”
- “How many failed jobs did we have yesterday?”
Good design:
- map user intent to queryable dimensions
- generate or select the query
- validate the query or use a safe template
- execute
- synthesize the answer
Pattern 2: Tool-based QA
Use when the answer belongs to APIs or business systems rather than one queryable table.
Best for:
- order status
- account details
- provider limits
- workflow status
The model should decide what to fetch, but the fetched result should remain the source of truth.
Pattern 3: Retrieval-based grounding
Use when the answer depends on human-written documentation, policies, or reports.
Best for:
- support policy
- product documentation
- internal procedures
In these cases retrieval matters, but citation and evidence selection still come before fluent answer generation.
A practical grounding decision tree
Ask:
- Is the answer in a system of record?
- Is that system queryable or callable?
- Does the user need raw values, an explanation, or both?
If the answer is structured and live, use a tool path first.
If the answer is narrative and document-based, use retrieval.
If the user needs both numbers and explanation, fetch the numbers deterministically and let the model explain them second.
How to evaluate grounded QA
Measure:
- answer correctness
- citation or evidence correctness
- unsupported-claim rate
- query or tool success rate
If a system can answer gracefully but not correctly, it is not ready.
Common failure modes
- asking the model to answer directly from memory
- using RAG where SQL or tools are safer
- not validating tool outputs before synthesis
- mixing live data and stale context in one answer
Grounding worksheet
For one QA workflow, define:
- What is the system of record?
- Is the answer live, historical, or policy-based?
- What should be fetched deterministically?
- What should the model only explain, not invent?
- What evidence should be shown back to the user?
How StackSpend helps
Grounded QA systems usually involve distinct cost layers: model usage, retrieval, and tool calls. Tracking spend by workflow makes it easier to see whether the grounded path is staying efficient as usage grows.