Chapter 10: Measuring Success: Solo + Team Metrics Without Fake Precision
February 3, 2026
·
2 min read

Series: LLM Development Guide
Chapter 10 of 15
Previous: Chapter 9: Stop Rules + Pitfalls: When to Upgrade, Bail, or Go Manual
Next: Chapter 11: Team Collaboration: Handoffs, Shared Prompts, and Review
What you’ll be able to do
You’ll be able to tell, with reasonable honesty, whether the workflow is helping:
- Pick a small set of metrics you can actually measure.
- Separate leading indicators (process) from lagging indicators (outcomes).
- Avoid fake precision and vanity metrics.
TL;DR
- If you can’t measure reliably, don’t invent numbers.
- Track a baseline (a few representative tasks) before you claim improvement.
- Favor cheap metrics: time to first commit, PR revision rounds, post-merge bugs.
- Use leading indicators daily; use lagging indicators in retros.
Table of contents
What to measure
Pick a small set that maps to real outcomes.
Velocity indicators:
- Time to first commit.
- Phase completion time.
- PR cycle time.
Quality indicators:
- PR revision rounds.
- Bugs caught in review.
- Post-merge bugs.
Efficiency indicators:
- Rework rate (time fixing output vs total time).
- Session count per task.
- Handoff success (can someone else continue without re-explaining).
Solo baseline
If you’re working solo, you can still create a baseline.
Track per task:
- Start time.
- First commit time.
- Total time to done.
- Number of “LLM retries” (how many prompt iterations for the same logical unit).
- Bugs you found after “done”.
The point is not perfect measurement. The point is noticing patterns.
Leading vs lagging indicators
Leading indicators predict success:
- Work notes are updated.
- Prompts contain verification.
- Commits are atomic.
- References are provided.
Lagging indicators confirm success:
- PR merged with low rework.
- Low post-merge bug rate.
- Handoffs succeed.
Lightweight reporting template
## LLM-Assisted Development Summary (Month)
### Adoption
- Tasks completed with workflow: <N>
### Velocity
- Median time to first commit: <X>
- Median PR cycle time: <Y>
### Quality
- Median PR revision rounds: <Z>
- Post-merge bugs: <N>
### Costs
- LLM cost estimate: <X>
### Notes
- What worked:
- What failed:
- Changes for next month:
Verification
Keep a simple CSV so you can graph later if you want.
mkdir -p work-notes
cat > work-notes/metrics.csv <<'CSV'
date,task,time_to_first_commit_minutes,total_time_minutes,llm_retries,pr_revision_rounds,post_merge_bugs,notes
CSV
Expected result:
- You can append one row per task in under a minute.
Continue -> Chapter 11: Team Collaboration: Handoffs, Shared Prompts, and Review
Authors
DevOps Architect · Applied AI Engineer
I’ve spent 20 years building systems across embedded firmware, security platforms, fintech, and enterprise architecture. Today I focus on production AI systems in Go — multi-agent orchestration, MCP server ecosystems, and the DevOps platforms that keep them running. I care about systems that work under pressure: observable, recoverable, and built to last.