I just attended our first Genie Community office hour and one of the few topic raised was around how to address Genie answers.
This is one thing I still think we do not talk about enough.
What happens after the demo or PoC is complete and we decided to roll it out?
Now that a Genie Space is live and a business user says: “that answer is wrong.”, what should we do?
Genie Spaces are a domain-specific natural-language interface over governed data, and the docs are very explicit that quality comes from curation: datasets, SQL examples, SQL expressions, and instructions tuned to your domain.
The part I find most useful in practice is to stop treating bad answers as one generic problem. Most of the time, the issue falls into one of a few buckets the docs already call out:
business jargon mismatch
wrong table or column selection
filtering on the wrong value
incorrect joins
metric-definition issues
token pressure in larger spaces or longer conversations
I never had to curate a large scale deployment of a Genie space, but during my time at SAP, I played quite a bit with recast.ai, so I’m using this experience and discussions with customers here.
My triage playbook would be pretty simple (and sometimes if not too often simple solution are the most efficient ones):
First, inspect the generated SQL and ask whether the problem is semantic or structural. The monitoring guidance recommends reviewing the generated SQL, editing it when needed, and saving corrected queries back as instructions so the Space learns from that pattern.
If Genie misunderstood the business language, add instructions or synonyms. The curation and troubleshooting docs both emphasize bridging the gap between general language and company-specific terminology.
If Genie picked the wrong data, reduce ambiguity by tightening descriptions, hiding irrelevant columns, removing overlapping objects, or giving better example SQL.
If the answer is numerically wrong, assume a metric-definition issue before assuming the model is “bad.” The docs recommend encoding business logic in SQL expressions and example rollups so Genie applies the same measure definitions consistently.
If the problem shows up repeatedly, add a benchmark and track it. Genie monitoring supports benchmarks, response review, and ongoing tuning as questions evolve.
A few implementation habits seem especially important to me:
start smaller than you think you need
keep the Space focused
use metric views or pre-joined data when the raw model is too noisy or too complex
treat Space quality as an iterative process, not a one-time setup
I also think this becomes even more important with Agent mode. Agent mode can do multi-step reasoning, run multiple SQL queries, and return a fuller report with citations and visualizations, but it still depends heavily on the quality of the underlying Genie Space context.
If you are embedding Genie in apps or building operational workflows around it, the Conversation and Management APIs are also worth a look.
The Genie Spaces API supports stateful natural-language conversations, while the management side supports programmatic creation, configuration, and deployment workflows across workspaces.
I’m curious how others are handling this at their company: when a Genie answer is wrong, what is your first move? Do you usually fix it with instructions, SQL examples, semantic modeling, or benchmarks?
A few docs that shaped how I think about this:
Curate an effective Genie Space for the “start small, iterate, and tune” mindset.
Troubleshoot Genie Spaces for concrete failure modes and fixes.
Agent mode in Genie Spaces for what changes when questions become more exploratory or multi-step.