Granola Performance Tuning
Overview
Optimize Granola output quality across three dimensions: audio/transcription accuracy, AI enhancement quality, and integration speed. Granola's AI (GPT-4o/Claude) produces better output when it has clean audio, well-typed notes, and structured templates.
Prerequisites
- Working Granola installation with meetings captured
- Willingness to improve audio setup and meeting practices
- At least 3-5 meetings captured to establish baseline quality
Instructions
Step 1 — Optimize Audio for Transcription
Granola captures system audio from your device. Transcription accuracy depends entirely on audio quality:
Hardware recommendations (by priority):
| Setup | Accuracy Impact | Recommendation |
|---|---|---|
| Wired headset with mic | Highest | Best for solo/remote meetings |
| USB condenser mic | High | Best for in-office, multiple speakers |
| Laptop built-in mic | Medium | Acceptable for quiet environments |
| Bluetooth headset | Variable | May cause dropouts — test first |
| Speakerphone in room | Low | Echo and distance degrade accuracy |
Audio configuration checklist:
- Correct input device selected in System Settings > Sound > Input
- Input volume at 75-100% (not too low, not clipping)
- Audio enhancements disabled (Windows: right-click device > Properties > disable enhancements)
- No conflicting virtual audio software (Loopback, BlackHole, etc.)
- Bluetooth device stable (or switch to wired if experiencing drops)
Room setup:
- Minimal background noise (close doors, turn off fans)
- Soft surfaces to reduce echo (avoid glass-walled conference rooms)
- Mic within 12 inches of speaker(s)
- Meeting participants using headsets (reduces echo and crosstalk)
Step 2 — Improve Meeting Practices
These behaviors directly improve Granola's output:
| Practice | Impact | Why It Helps |
|---|---|---|
| State names when assigning work | High | "Sarah, can you handle the API spec?" enables correct attribution |
| Use explicit action language | High | "Action item: review by Friday" — AI detects structured language |
| One speaker at a time | High | Crosstalk confuses speaker diarization |
| Summarize decisions verbally | Medium | "So we've decided to go with option B" — AI captures decisions |
| Spell technical terms first time | Medium | "We'll use Kubernetes, K-U-B-E-R-N-E-T-E-S" — improves accuracy |
| Type notes during the meeting | High | Your notes give the AI critical context for enhancement |
| Brief recap at meeting end | Medium | "To summarize, we agreed on X, Y, and Z" — improves summary |
Step 3 — Optimize Templates for AI Quality
Template structure directly affects the quality of enhanced output:
High-quality template design:
## Summary
[2-3 sentence overview of the meeting]
## Key Decisions
[Bullet list of decisions made, with reasoning]
## Action Items
[Format: - [ ] @person: task (due date)]
## Open Questions
[Items that need follow-up or weren't resolved]
## Next Steps
[What happens after this meeting]Template optimization tips:
- Use 5-7 sections max — too many sections dilute content
- Include format hints —
[Format: - [] @person: task]guides the AI - Put Action Items near the end — AI processes sequentially, actions at the end capture the full meeting
- Add "Verbatim Quotes" section for customer calls — AI will pull exact language from the transcript
- Avoid generic sections — "Notes" and "Discussion" produce vague output; be specific
Step 4 — Post-Meeting Quality Review (5 Minutes)
After enhancing notes, spend 5 minutes on quality assurance:
- Summary accurate? Does it reflect what actually happened?
- Action items complete? Are all commitments captured with correct owners?
- Decisions correct? No hallucinated decisions or mixed-up attributions?
- Sensitive content? Remove anything that shouldn't be shared before posting
- Missing context? Add background the AI couldn't know
Step 5 — Use Granola Chat to Fill Gaps
After enhancement, use Chat to improve the notes:
"What did Mike say about the timeline?"
→ Searches transcript for Mike's statements about timeline
"Were there any disagreements that aren't captured in the summary?"
→ Analyzes transcript for conflicting viewpoints
"Add the budget numbers that were discussed"
→ Pulls specific figures from the transcript
"Rewrite the action items with more detail"
→ Expands terse action items with transcript contextStep 6 — Measure and Track Quality
| Metric | Target | How to Measure |
|---|---|---|
| Transcription accuracy | >95% word accuracy | Spot-check 2-3 min of transcript vs. audio |
| Action item detection | >90% captured | Compare enhanced notes to manual list |
| Decision accuracy | 100% correct | Verify all listed decisions actually happened |
| Processing time | <2 min for 30-min meeting | Timestamp when meeting ends vs. when notes are ready |
| Enhancement usefulness | 4+/5 team rating | Monthly survey: "How useful are Granola notes?" |
Track these monthly. If accuracy drops below target:
- Check audio setup (most common cause)
- Review template structure
- Verify meeting practices are being followed
- Contact Granola support for persistent issues
Output
- Audio setup optimized for maximum transcription accuracy
- Meeting practices improving AI output quality
- Templates structured for effective enhancement
- Quality measurement process established
Error Handling
| Issue | Cause | Fix |
|---|---|---|
| <85% transcription accuracy | Poor microphone or noisy room | Upgrade to wired headset, reduce background noise |
| Action items missed | Vague language ("someone should...") | Use explicit format: "Action item: @person does X by Y" |
| Wrong speaker attribution | Crosstalk or no name usage | State names, avoid overlapping speech |
| Slow processing (>5 min) | Long meeting or server load | Normal for 2+ hour meetings; check status.granola.ai |
| Hallucinated decisions | AI filling template sections | Review before sharing; remove decisions that didn't happen |
Resources
Next Steps
Proceed to granola-cost-tuning for cost optimization and plan selection.