Databricks AI functions: check SQL labels and summaries

To evaluate Databricks AI functions in SQL, start with a small table of reviewed examples and compare each generated label or summary with its source text. Keep the tested query, chosen settings, and failures before expanding the run. This is an evaluation workflow, not a SQL function reference: confirm the exact function signature and availability before executing it. fenbs can keep the checked method and next steps outside the current AI conversation.

The linked Databricks Genie overview↗ (opens in a new tab) describes governed AI experiences, and its Genie Code guide↗ (opens in a new tab) covers coding assistance. Those guides are not a function-by-function SQL reference. Do not substitute a plausible generated function name or model setting for documentation of the expression you intend to run.

How do you start with a small SQL test?

Choose one task: assign a topic label to a message, or produce a short summary of it. Keep those as separate experiments at first. A correct label does not prove the summary preserves all important facts, and a readable summary does not prove your categories are well defined.

1. Choose an approved test table with a stable row identifier and text you are permitted to process. Prefer invented examples while you establish the method.
2. Write the expected label or essential summary facts for each row before running the AI expression. Have a person review ambiguous examples.
3. Check the chosen function’s vendor reference for its exact syntax, supported environment, input rules, and available settings. Stop if any requirement remains unresolved.
4. Prepare a bounded query that returns the identifier, input, expected result, and generated result together. Review its scope before execution.
5. Run only the approved test, inspect the returned rows, and record failures before deciding whether a larger evaluation is justified.

This sequence avoids a common evaluation mistake: seeing several reasonable outputs and assuming the whole task works. Give the test a clear purpose, such as “distinguish delivery questions from return requests.” A narrow purpose makes disagreements easier to investigate than a general instruction to understand customer messages.

What should your reviewed examples include?

Use clear cases, ambiguous cases, and inputs where there is no useful answer. The examples below are invented. They illustrate how to define an expected result without copying customer records into your evaluation notes. Change the categories to match the task you actually need to perform.

Write down what to do when a message belongs to more than one category. Either choose a documented priority rule for your test or allow a review outcome. Do not quietly change the expected answer after seeing the generated one. If people disagree about the right label, resolve the definition before counting the model’s response as an error.

For summaries, list the facts that must survive: the request, any stated deadline, and any uncertainty. Also list facts that must not appear, such as an invented promise of a refund. Judge against the input rather than against how polished the output sounds.

How can you prepare the query without running it?

If you use coding help, open the Genie Code pane from the upper-right corner of the workspace page and enter a prompt. That is the documented Genie Code entry point↗ (opens in a new tab). Ask it to explain the proposed query and identify assumptions before you authorize execution.

Explain; don’t run code.
Review this proposed SQL evaluation against the function reference I provide.
Keep the stable row ID, source text, expected answer, and generated answer visible.
Identify unverified syntax, settings, permissions, and output assumptions.
Do not expand the input table or write results until I approve the query.

Genie Code can run code, and the documentation describes both automatic action assessment and user approval options. Its capability guide↗ (opens in a new tab) documents “explain; don’t run code” as a request for conversation. Review the action settings as well; a written instruction is not a replacement for controlling execution.

Keep this task distinct from connecting an outside assistant to data. The Claude Databricks connector guide and Databricks MCP overview address that separate connection. Neither replaces the SQL reference or an output evaluation.

How do you judge the returned rows?

Inspect the first result row by row. Check that every expected identifier appears once, that source text still matches the identifier, and that missing outputs are visible. A classification report can look strong while silently excluding the very records that failed to produce a result.

• Labels: compare each returned value with the allowed categories and the reviewed answer. Keep unexpected values separate from ordinary disagreements.
• Summaries: check every factual statement against the input. Mark missing deadlines, changed meanings, and additions separately.
• Coverage: count missing, empty, and failed results instead of removing them from the test report.
• Ambiguity: preserve cases that need a person’s judgment. They reveal where the task definition needs work.

Avoid one overall score as your only record. A method that handles simple delivery questions but confuses every return request needs a different change from one that produces occasional blank results. Save representative failures with invented or approved text, so the next reviewer can understand the problem without rerunning everything.

What should you change when the test fails?

Change one part at a time: the category definitions, the task wording, or a documented setting. Keep the same reviewed rows for comparison, then add fresh examples to check whether the improvement holds. Record the exact query used for each run instead of overwriting the earlier version.

Record any model choice that the function actually exposes, along with other explicit settings. If the function does not expose a setting, do not invent one for consistency with another tool. If you did not record a value, mark it unknown. This makes a later difference an investigation rather than an invitation to guess.

For persistent AI instructions, see Databricks agent memory. For this evaluation, the reusable record is the tested question, approved SQL, checked results, and remaining uncertainty. fenbs can keep that work and its next step together when you change assistants.

What access and saving limits should you check?

Databricks documents Genie Code as governed by the user’s data permissions, with account, workspace, and region controls affecting its capabilities. That does not establish eligibility for an arbitrary SQL AI function. Check function-specific requirements and your organization’s approved processing rules before moving beyond invented test data.

Keep source records and full result tables in their approved data environment. Save only permitted evaluation notes elsewhere. A pasted transcript is rarely the clearest record: include the query location, run scope, checked outcome, and what a person still needs to review.

How to keep your evaluation work with fenbs

Keep the method and checked findings so you can carry on from exactly where you left off, on any device and in another AI. Leave the underlying data where it belongs. A short, accurate record is easier to use than a long conversation with several conflicting query drafts.

1. Create a free account.
2. Save the task you tested, a link to the approved query, and the examples you are allowed to share.
3. Connect Databricks Genie Code to fenbs using Integrations. Ask the person who manages your workspace to approve the connection if needed.
4. Write down what passed, what failed, and the next check. Ask your connected assistant to read it before continuing and save progress when it stops.

Genie Code’s MCP guide↗ (opens in a new tab) describes selecting registered services subject to workspace permissions. fenbs does not run your SQL or replace the result table. If you only need stored query outputs, keep them in the data workspace. More AI at work guides cover carrying reviewed work between sessions.