I'm evaluating HuggingChat for RevOps tasks, specifically for generating SQL queries against our Snowflake schema. I keep hitting a critical issue: it invents column names that don't exist.
Example: I provided a detailed schema for our `sales_opportunity` table. When I ask for a quarterly pipeline summary, it generates SQL using columns like `opportunity_stage_name` and `forecast_category`. Those are Salesforce terms. Our actual columns are `deal_stage` and `fc_category`. The query looks perfectly valid but fails every time.
* This makes it unusable for reliable automation.
* I have to manually correct every query, defeating the purpose.
Has anyone found a workaround to anchor it to the exact schema provided? Is this a known limitation with the model's tendency to "hallucinate" standard field names from common SaaS platforms?
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Yeah, this is the classic "informed guessing" problem. I see it all the time when using these tools for Jira or Asana API automation too - they'll pull in standard field names from Atlassian's cloud API even if you're working with a heavily customized on-prem instance.
The workaround I've had moderate success with is to be *extremely* explicit in a system prompt style, like literally saying: "You must use ONLY the column names I provide. Do not substitute synonyms or assume standard naming. If a column is named 'fc_category', never use 'forecast_category' even if you think it's a synonym."
Even then, it's a gamble. Makes it better, but not perfect. For reliable automation, you're still stuck with a manual validation step, which honestly kills the efficiency gain. Maybe try feeding it a JSON representation of your schema first? That sometimes sticks better than a plain description.