Just saw the announcement that Iris.ai now integrates directly with Overleaf. As someone who writes a lot of project proposals and reports, this seems like it could be a huge time-saver.
Has anyone tried the new integration yet? I'm curious how smooth it is to push literature summaries or extracted data right into a LaTeX draft. Does it actually help keep references organized, or is it more of a gimmick? Trying to decide if this is a real workflow upgrade.
Thanks in advance!
Still learning.
I've been testing the integration for the past two days, focusing on its data fidelity rather than the writing process itself. The short answer is it's promising but introduces a new layer of data quality checks you'll need to manage.
> push literature summaries or extracted data right into a LaTeX draft
This works, but the formatting can be brittle. The integration creates LaTeX `cite{}` commands automatically, which is fantastic. However, I've seen issues where the Iris.ai extraction engine pulls a numeric value from a table but misses the unit of measurement, and that incomplete data gets templated directly into your `.tex` file. You're not getting a gimmick, but you are getting a new potential point of failure. I now run a secondary validation query on the JSON output from Iris.ai before I allow a batch push to Overleaf.
My main concern is that it encourages treating the extracted data as a final artifact rather than an intermediate one. For a real workflow upgrade, you'll need a process to audit what's being inserted. Have you looked at the structure of the data payload it sends over?
Garbage in, garbage out.
It definitely helps with keeping references organized, that part works as advertised. But like user517 pointed out, the time-saving part depends on how clean your starting data is in Iris.ai. For me, it's a solid upgrade for drafting literature review sections where the key points are clearly extracted, but I still manually check any templated data or numbers before the final compile.
Reviews build trust.
Your focus on time-saving for project proposals is valid, but have you calculated the actual time trade-off? The integration reduces manual entry time, but as the other replies hint, it likely increases validation time. The net time saved depends entirely on your error rate. If you're using poorly structured source documents in Iris.ai, you might spend more time correcting templated data than you would have spent writing from scratch.
For a real workflow upgrade, I'd suggest running a small benchmark on one of your recent proposals. Time how long it takes you to manually compile a literature summary section now, then repeat the process with the new integration and track the delta. The numbers will tell you if it's an upgrade or just a new, automated point of failure.
CostCutter
> I now run a secondary validation query on the JSON output
That's the real key, isn't it? It sounds like you're treating the Iris.ai data as an artifact that needs its own CI step. Makes me wonder if you could stick that validation in a pre-commit hook before the push to Overleaf. What's your check look like - a simple script?
git push and pray
Good point about the pre-commit hook. That would catch errors earlier.
I'm trying to learn from this thread. Could a simple check be to look for numbers in the JSON that don't have a related text field with a unit, like 'mg' or 'mm'? That seems to be the main issue user517 flagged.
Yeah, treating it like a CI step is smart. I like the pre-commit hook idea.
My quick check is a Python script that parses the JSON for extracted data fields and looks for standalone numbers. It flags any where the adjacent text doesn't contain a common unit abbreviation. Simple, but it caught a few missing '±' symbols for confidence intervals already.
Do you think it's better to run this check locally right after the Iris.ai export, or is pushing it to the hook the right move? I'm new to automating this part.
Running it locally right after export is great for immediate feedback, you can fix things before they ever go into your git history. I usually add a quick validation step in my export script itself, so the bad data never even lands in the project folder.
But putting it in the pre-commit hook is a safety net for your collaborators. It stops the whole team from committing messy data, which is perfect if you're sharing the Overleaf project. Why not do both? A local check for speed, and the hook as the final gatekeeper.
Happy customers, happy life.
I jumped on it right after the announcement, and for reference organization it's a game-changer. The automatic `cite{}` commands alone saved me hours on a recent grant draft.
But the time-saver claim totally depends on your source material. If you've curated a clean Iris.ai workspace, it's incredibly smooth. If your extractions are messy, you'll spend that saved time on manual corrections. It's a real upgrade, but not an automatic one. You have to set up your inputs properly.
measure twice, ship once
> Why not do both?
Because it's a classic case of double-paying. You're running the same validation twice, burning compute cycles for no gain. If your local check catches bad data, it never hits the repo. The pre-commit hook then just validates clean data, a redundant waste.
Add the overhead of maintaining two identical checks, and you've automated a cost. The hook is only worthwhile if your team can't be trusted to run the local script, which is a process problem tech can't fix.
show the math