Great advice on starting with a clear use case and a mixed team. That sounds much less daunting than trying to evaluate everything at once.
I'm curious, how did you handle the trial pricing? Did you do the free tier for the pilot, or go straight to a paid trial to compare the plans for your specific use cases? I'm always worried about hitting a feature limit mid-pilot and skewing the results.
Still learning.
Hah, the classic "what worked for us" anecdote. That's precisely the problem.
You're assuming your pain points are their pain points. Starting with "get clear on your main use case" is a trap because it assumes you already know what the tool will be good for. You don't. You'll unconsciously steer the pilot toward confirming your biases.
The mixed team is the only solid advice here, but you missed the most critical member: your most cynical, detail-obsessed engineer. The one who hates magic boxes. Their logs of "distracting" suggestions will be ten times more valuable than the marketing ops person's log of "time saved" on boilerplate. The boilerplate is the easy win, and it's meaningless.
If you really want to track something, track the *latency* of the bad suggestions. How long did it take the dev to realize the perfectly valid-looking code was wrong for your context? That's the real cost.
Data skeptic, not a data cynic.
> "Track specific pain points it solves (or doesn't)."
This is the right instinct, but my team found we had to define "pain point" super specifically to get good data. "Useful vs distracting" was too vague and led to inconsistent notes.
We tracked "time to first correct suggestion" for our common CRM scripting patterns, like building a lead scoring update in HubSpot. If Tabnine gave us the right structure in one or two keystrokes, that was a clear win. If it took three ignored suggestions to get there, we logged that as friction.
Also, on your mixed team point: include someone who *doesn't* write code daily, like a content manager who occasionally tweaks email templates. Their fresh eyes caught "correct-looking" suggestions that used deprecated variables the rest of us had tuned out.
Keep it simple.
Exactly. Defining objective criteria upfront is key, but you can take it a step further with your metrics. Track the edit distance for "useful" suggestions you do accept.
If a suggestion for a standard API call is accepted but then requires 20 character edits to match your internal auth pattern, was it truly useful? That's different from a suggestion accepted with zero edits. The first one might still save time, but it reveals a pattern mismatch the binary "accepted/ignored" log misses.
Latency is the enemy, but consistency is the goal.
Edit distance is a proxy for time, and time is the only metric that matters for a tool like this.
But you're still measuring the wrong thing. You're measuring the tool's accuracy against your own patterns. The real cost is the cognitive load of evaluating a "mostly right" suggestion. You have to parse it, spot the 20-character mismatch, and fix it. That mental switch is the distraction.
Sometimes typing the whole thing from scratch is faster because the context stays in your head. Track that instead.
Simplicity is the ultimate sophistication
Mixed team is right, but you're missing the critical member. Need your most skeptical engineer who hates AI tools. They'll spot the subtle issues others glaze over.
The free tier is fine for a pilot, but cap the trial at one sprint. If you hit a feature limit, that's useful data too.
Tracking useful vs distracting is pointless without defining "distracting." Is it a wrong suggestion, or just one that breaks your flow? Big difference.
I agree completely on including the team skeptic, but they need a framework for their criticism to be productive. Without one, you'll just get "this is garbage" notes.
Pair them with the tracking method user1314 mentioned, where a suggestion is only logged as useful if accepted without edits for a known pattern. The skeptic's job is to rigorously defend that definition. Their natural inclination to distrust the tool makes them perfect for catching those "almost right" suggestions that break flow but don't technically fail a binary test.
Hitting a free tier limit mid-sprint isn't just data, it's a concrete stress test for the tool's value proposition. Can you still complete the core pilot use case, or does work grind to a halt?
Commit early, deploy often, but always rollback-ready.