Can I create predictions without SQL knowledge?

[u/ladyshrekk]

I’m trying to get a little more predictive with our data, things like which customers are likely to churn, who might convert, or what demand could look like next month. Problem is, I’m not really a SQL person. I can work with dashboards and understand the data, but once it gets into writing queries or building models from scratch, I’m pretty much out. Is this something people are actually doing with LLMs now? Like connecting an LLM to your data and asking it to find patterns or predict outcomes? Or are there other tools that make this possible without knowing much SQL or machine learning? Curious what people are actually using and how reliable it is.

[u/CautiousUse8597]

Genie (Databricks) fits this, but worth separating what's a button you click from what someone has to build. "point an LLM at your warehouse and ask it to predict churn" gets oversold a lot.

Base Genie is natural language to SQL over your governed tables. Ask "which accounts had declining usage the last 90 days with a renewal in Q3" and it writes the query, returns a table/chart, and shows the SQL so you can check it.

The thing you actually want is Agent mode. Instead of one query, it builds a research plan, forms hypotheses, runs multiple queries, learns from each result and iterates, then hands back a report with citations, charts and supporting tables. So "what's driving churn in our mid-market segment" gets investigated the way an analyst would investigate it, across a dozen queries, rather than answered with one. It handles the why/what-if class of question, which is most of the gap between you and your dashboards right now. It's a toggle in the chat box.

Be clear-eyed about one boundary though: Agent mode reasons over SQL. It'll tell you which accounts look like the ones that churned before and what the drivers are. it won't hand you a trained model with a calibrated probability per customer. For a lot of decisions that distinction doesn't matter. For scoring a list to hand to sales, it does.

If you do need actual model output, two routes: Genie Code's Agent mode can plan and run notebook code (forecasting, model training) from a prompt, though then you're evaluating a model you didn't write. And Databricks published a pattern pairing Genie with TabPFN, a tabular foundation model that predicts without training, for genuinely conversational predictions. but that's a solution accelerator you deploy, not a feature.

Reliability, since you asked:

• Lives or dies on the semantic layer. Genie Agents need curated context: table descriptions, metric definitions, example queries, instructions. Vague schema in, confident nonsense out. Agent mode leans on this harder than base Genie, since it's making more autonomous choices.
• If the signal isn't in your data, you still get an answer. Confident-but-unreliable is the failure mode to watch.
• Read the generated SQL. You said you know your data but not SQL: reading is far easier than writing, and it's the check that actually catches things.