Hey everyone! 👋 I've been using Fireflies.ai to capture our team's cloud architecture meetings for a few months now, and it's been a game-changer. But let's be real—it kept tripping over our internal lingo. It was turning "S3" into "ess three" and completely mangling acronyms like "TGW" (Transit Gateway) or our project code names.
The good news is you *can* train it! It's not a built-in feature you click, but a process of consistent correction. Here's the workflow that worked for our DevOps squad:
**Step 1: Build Your Glossary File**
Start with a simple CSV. Fireflies learns from your corrections in the notebook, but feeding it a list helps. I structured mine like this:
```csv
Phrase, Meaning, Context
S3, Amazon Simple Storage Service, AWS storage service
IaC, Infrastructure as Code, Terraform or CloudFormation
TGW, Transit Gateway, AWS networking
"Project Phoenix", Our legacy migration initiative, Internal project name
```
**Step 2: The Correction Ritual**
After every meeting, review the transcript and **use the 'Notebook' feature to correct terms**. This is crucial. The AI learns from these manual edits over time.
* Find a mis-transcribed term (e.g., "E C Two").
* Correct it to "EC2".
* Add a brief note in the notebook comment like "AWS EC2 instance service".
**Step 3: Share with Your Team**
Get your engineers to do the same! The more everyone corrects the same terms, the faster Fireflies learns your team's unique vocabulary. We made it a quick post-standup task for the note-taker.
It took about 2-3 weeks of consistent corrections before we saw a dramatic improvement. Now it nails even our weirdest internal acronyms! This made search so much more valuable—finding all discussions about "TGW routing" actually works.
Has anyone else tried a similar approach? I'd love to hear if you found a better way to batch-train it or handle really obscure terms!
~CloudOps
Infrastructure as code is the only way