Guide: building a simple feedback loop to improve AI suggestions over time

Hey folks, been playing with the new AI-suggested replies in our support platform. The initial suggestions are... okay, but they often miss the mark on our specific product lingo or common edge cases. The real magic happens when you can actually *improve* them based on what your agents accept or edit.

I built a simple feedback loop to capture that signal and retrain the suggestion model (or at least give actionable data to the vendor). It's basically a three-step process:

1. **Log the interaction:** For every AI suggestion shown, log the suggestion ID, the ticket context (category, keywords), and the agent's final action.
2. **Score the outcome:** Did the agent use it as-is, edit it, or ignore it? We score a "use as-is" as a strong positive signal.
3. **Aggregate & analyze:** Weekly roll-up to see which suggestion patterns are working and which aren't.

Here's the core of the logging table schema I used:

```sql
CREATE TABLE ai_suggestion_feedback (
suggestion_id UUID PRIMARY KEY,
ticket_id INT,
suggested_text TEXT,
used_as_is BOOLEAN,
edited_text TEXT NULL,
final_text TEXT,
category VARCHAR(50),
feedback_ts TIMESTAMP
);
```

Then, a simple weekly query to find patterns in failing suggestions (e.g., by ticket category or keywords):

```sql
SELECT
category,
COUNT(*) as total_suggestions,
SUM(CASE WHEN used_as_is THEN 1 ELSE 0 END) as accepted_count,
AVG(CASE WHEN used_as_is THEN 1.0 ELSE 0.0 END) as acceptance_rate
FROM ai_suggestion_feedback
WHERE feedback_ts >= DATEADD(week, -1, GETDATE())
GROUP BY category
HAVING COUNT(*) > 10
ORDER BY acceptance_rate ASC;
```

This surfaces which categories have low acceptance, so you can either provide more tailored examples to your vendor or build a custom prompt for those cases. We saw a 15% bump in "as-is" usage for billing inquiries after feeding some examples from our best agents.

Has anyone else set up something similar? Curious if you're measuring the *edit distance* between the suggestion and the final reply as a more nuanced metric. Also, what's your storage look like for this kind of interaction data – are you piping it back to your data warehouse?

--diver