What is granola-performance-tuning?

The Granola Performance Tuning skill is a comprehensive optimization guide designed to maximize the accuracy of AI-driven transcriptions and the quality of meeting notes. It provides actionable strategies across hardware setup, environmental control, meeting behaviors, and template structures to ensure high-fidelity documentation and efficient post-meeting workflows.

When should I use granola-performance-tuning?

granola-performance-tuning is useful in the following scenarios: • Optimizing audio hardware and room acoustics to achieve up to 98% transcription accuracy for critical business meetings. • Implementing verbal best practices during meetings, such as stating names and summarizing decisions, to improve AI action item extraction. • Reducing note processing latency by managing environmental factors and optimizing internet connectivity for faster uploads. • Training the AI model to recognize specialized industry jargon and technical terminology through custom vocabulary techniques. • Streamlining cross-platform productivity by integrating optimized Granola notes with Zapier, Notion, and Slack for automated task management.

name	granola-performance-tuning
description	\|
allowed-tools	Read, Write, Edit
version	1.0.0
license	MIT
author	Jeremy Longshore <jeremy@intentsolutions.io>

Granola Performance Tuning

Overview

Optimize Granola for best transcription accuracy and note quality.

Transcription Quality Factors

Audio Quality Hierarchy

Transcription Accuracy
        ↑
[Professional Microphone] 98%
        ↑
[Quality Headset Mic] 95%
        ↑
[Laptop Built-in Mic] 85%
        ↑
[Phone Speaker] 70%

Environmental Factors

Factor	Impact	Optimization
Background noise	High	Use quiet room, noise cancellation
Echo/reverb	High	Soft furnishings, smaller room
Distance from mic	Medium	Within 12 inches of microphone
Multiple speakers	Medium	Use identification phrases
Accent variation	Low	Improves over time with usage

Audio Setup Optimization

Recommended Equipment

## Microphone Recommendations

Budget (~$50):
- Blue Snowball iCE
- Fifine K669

Mid-Range (~$100):
- Blue Yeti
- Rode NT-USB Mini
- Audio-Technica AT2020USB+

Professional (~$200+):
- Shure MV7
- Elgato Wave:3
- Rode PodMic + interface

Microphone Settings (macOS)

# Check current input device
system_profiler SPAudioDataType | grep -A5 "Default Input"

# Adjust input volume (System Preferences)
# Aim for: Input level peaks at 75% during normal speech

Room Optimization

## Environment Checklist
- [ ] Close windows to reduce outside noise
- [ ] Turn off fans, AC if possible
- [ ] Use soft surfaces (carpet, curtains)
- [ ] Position away from keyboard clicks
- [ ] Mute when not speaking

Note Quality Optimization

Meeting Preparation

## Pre-Meeting Checklist
- [ ] Share agenda in advance
- [ ] Send attendee list to calendar
- [ ] Prepare context notes in template
- [ ] Test audio before meeting

During Meeting

## Best Practices
1. State names when addressing people
   "Sarah, what do you think about..."

2. Summarize decisions verbally
   "So we're agreed: deadline is Friday."

3. Spell out technical terms
   "The API endpoint, A-P-I..."

4. Avoid crosstalk
   One person speaking at a time

5. Use clear action item language
   "Action item: Mike will review the PR by Thursday."

Post-Meeting Enhancement

## Note Review Checklist (5 min)
- [ ] Correct obvious transcription errors
- [ ] Add context AI might have missed
- [ ] Verify action items are complete
- [ ] Add links to referenced documents
- [ ] Tag key decisions

Template Optimization

Effective Template Structure

# Meeting Template: Sprint Planning

## Agenda (Pre-filled)
-

## Context
[Add links to relevant docs]

## Discussion Notes
[AI-enhanced during meeting]

## Decisions
- [ ] Decision 1: [Clear statement]

## Action Items
Format: - [ ] What (@who, by when)

## Follow-up
Next meeting: [date]

Template Best Practices

Practice	Reason	Impact
Use headers	Better AI parsing	+20% accuracy
Pre-fill context	Reduces ambiguity	+15% relevance
Standard formats	Consistent output	+10% usability
Action item format	Auto-extraction	+25% detection

Processing Speed Optimization

Factors Affecting Speed

Factor	Impact	Optimization
Meeting length	Linear	Expect 1 min processing per 10 min meeting
Internet speed	High	Ensure stable connection during upload
Peak times	Medium	Processing queue varies
Audio quality	Low	Cleaner audio = faster processing

Speed Expectations

Meeting Duration → Processing Time
15 minutes → 1-2 minutes
30 minutes → 2-3 minutes
60 minutes → 3-5 minutes
120 minutes → 5-8 minutes

Integration Performance

Zapier Optimization

## Reduce Zapier Latency

1. Use Instant triggers (not polling)
2. Minimize steps in Zap
3. Avoid unnecessary filters
4. Use multi-step Zaps efficiently
5. Monitor task usage

Batch Processing

# Instead of real-time, batch for efficiency
Schedule: Every 30 minutes
Process:
  - Collect all new notes
  - Batch update Notion
  - Single Slack summary
  - Aggregate CRM updates

Accuracy Improvement

Training the AI

## Improve Over Time

1. Correct errors when you see them
   - AI learns from corrections

2. Use consistent terminology
   - Builds vocabulary

3. Identify speakers
   - Improves attribution

4. Regular editing
   - Provides feedback loop

Custom Vocabulary

## Teach Domain Terms

Add to meeting intros:
"We'll discuss the OAuth2 implementation,
that's O-Auth-Two, and the GraphQL API,
spelled G-R-A-P-H-Q-L..."

Common terms to spell out:
- Acronyms (API, SDK, CI/CD)
- Product names
- People names with unusual spellings

Performance Metrics

What to Track

Metric	Target	How to Measure
Transcription accuracy	>95%	Sample review
Action item detection	>90%	Compare to meeting
Processing time	<5 min	Timestamp comparison
Note usefulness	4+/5	Team survey

Weekly Review

## Performance Check

Monday:
- [ ] Review last week's meeting notes
- [ ] Note common transcription errors
- [ ] Identify improvement opportunities
- [ ] Adjust templates if needed

Resources

Next Steps

Proceed to granola-cost-tuning for cost optimization strategies.

granola-performance-tuning

When & Why to Use This Skill

Use Cases