Chapter 10: Measuring Success: Solo + Team Metrics Without Fake Precision

February 3, 2026 · 2 min read
blog

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.