My acceptance rate for Codeium suggestions is hovering at 30%. That's the headline metric. It sounds low, but it's actually better than it seems.
The catch: I measure "acceptance" strictly. A partial acceptance (e.g., using 2 lines of a 5-line suggestion) doesn't count. My workflow is to only accept a full, correct suggestion. The 70% rejections are mostly:
* Over-engineered solutions for simple problems.
* Hallucinated APIs or incorrect syntax in niche libraries.
* It suggesting a change I explicitly just made (context window issue).
The real value isn't the 30% acceptance. It's the raw throughput. It spits out boilerplate faster than I can type, and the 30% that sticks saves real time. The other 70% is just a quick `Tab` then `Esc`.
Key benchmarks from my logs (custom instrumentation):
* **Avg. time saved per *accepted* suggestion:** ~45 seconds.
* **Avg. time lost per *rejected* suggestion:** ~2 seconds (to dismiss).
* **Net positive.** It's a tool, not a team member. Judge it on velocity, not intelligence.
—DD
Metrics don't lie.
That's a really practical way to look at it. I'd never thought to measure the time cost of a rejection, but framing it as a ~2 second loss makes the 30% rate look completely different. It's more about net time saved than a perfect success score.
Do you find your strict "full acceptance only" rule changes how you write code? I wonder if I'd start phrasing comments or structuring logic differently just to get a cleaner, complete suggestion from the tool.
Your point about it being a tool for throughput, not a team member, is spot on. Sets the right expectation.
That's a good question about writing code differently. I haven't consciously changed my style for it, but I've noticed I do think in more modular chunks now. If I'm about to write a standard API call handler, I might just stub out the function signature and let it fill in the repetitive parts. It's less about phrasing and more about recognizing which tasks are "its job" for raw throughput.
Have you found a specific comment pattern or structure that reliably triggers a correct, full suggestion? I'm curious if that's something you can actually optimize for, or if it's too inconsistent.
30% is a win. Everyone obsessing over acceptance rates is missing the point.
It's a glorified autocomplete. The 2-second tax on bad suggestions is cheaper than my coffee breaks. If you're measuring its "intelligence" you're using it wrong.
My benchmark: time to MVP. If it shaves hours off boilerplate, who cares if it hallucinates a library occasionally? Just hit esc.