Pattern 19: Improving Your AI Coding

How to Analyze Your AI Interactions and Get Better Over Time

Overview

Great AI coding isn’t magic—it’s a measurable skill. Treat chat logs like code reviews: find friction, kill repetition, improve prompts.

You’ll gain:

  • Which prompting tactics work for you
  • No more re-explaining context
  • Know what AI does well vs poorly
  • Ship faster with fewer messages

Serious about AI development? This is non-negotiable. Code reviews improve programming; conversation reviews improve AI piloting.

Key Principles

  1. Track patterns – Log repeated corrections
  2. Measure efficiency – Time to working code
  3. Experiment – A/B test prompts
  4. Share learnings – Team playbook saves hours

Exercise: Build Your Playbook

Step 1: Collect

Export 7 days of AI chats using SpecStory. Include successes, failures, and frustrating loops.

Step 2: Analyze

Tag patterns:

  • Repeat explanations (pasted same context twice)
  • Recurring errors (AI forgets your framework)
  • Surprise wins (one-shot success)

Step 3: Create Playbook

Write ai-playbook.md. List top 3 DOs and DON’Ts from your data.

Example:

## DO
1. Paste file tree before edits
2. Give <50 LOC chunks
3. State outcome before code

## DON'T
1. Say "make it better" without metrics
2. Paste entire stack traces
3. Mix multiple concerns

Step 4: Test

Validate your playbook with your next task. If it seems to help, keep the rule. Otherwise, refine or delete it.

Tools

  • SpecStory – Export Cursor & Claude logs
  • Helicone – Annotate and search history

Or build your own tool by parsing JSONL and grepping for your pain points.

Ship the playbook, ship the code, and repeat.