The first answer from an AI is rarely the final one. If you've ever worked with analytics tools, you've felt that gap. The AI gets you close, and then you nudge it into place. The interesting question isn't whether the first answer is perfect. It's whether the second answer is better because of what you taught it.
That's the whole promise of a feedback driven system: every correction is an investment. You're not just fixing an output. You're training a partner.
What a useful learning loop looks like
Early on, you ask, "Why did approval rate drop?" The AI gives you a quick change over change. You clarify that, for your team, approval rate uses the screened denominator, not total applications. The AI adjusts. Two weeks later, you ask a related question and it starts with the right definition. You didn't just get an answer. You raised the floor.
As the loop continues, the system starts to anticipate the parts you care about: whether there was a policy change mid‑month, which segments moved, if seasonality explains some of the variance. You spend less time correcting mechanics and more time interrogating the story.
Accuracy isn't a switch. It's a curve
When we put InsightAssist in front of early access teams, we saw a pattern repeat. Week one felt like a promising intern: fast, occasionally off, very coachable. By week four, many teams were getting answers that matched their internal definitions and expectations without a lot of prompting. By month three, the disagreements were about judgment calls, not basics.
The lift didn’t come from one huge model upgrade. It came from the quiet accumulation of context: how you define metrics, how you talk about them, what “normal” looks like in your business.
How to give feedback that compounds
You don’t need a new ceremony. The most helpful feedback is the kind you already give naturally:
- Be specific about the correction and why it matters. “Use screened denominator here because…”.
- Share brief context when you can. “Underwriting changed on the 15th, so split the month.”
- Be consistent once you've chosen a definition. Consistency accelerates learning.
Do that in the flow of your work and the system meets you where you are the next time.
Why this matters now
Analytics is moving from “generate a query” to “explain a change.” That requires memory, context, and a willingness to be taught. Tools that can’t learn stay useful for one‑off questions. Tools that do learn become part of the team.
If you want an assistant that improves with you, and because of you, that's what we're building.