Learn / Workflow Engineering / Quality and Iteration

Quality and Iteration

12 min apply 4 sections
Step 1 of 4

WHY WHY This Matters

AI doesn't know when its output is wrong. It can produce confident, well-formatted nonsense. Without quality controls, you're gambling with every AI output.

The quality imperative:

  • AI outputs need verification (it doesn't self-correct)
  • Early feedback is cheaper than late discovery
  • Iteration is often faster than perfect prompts
  • Quality gates protect downstream work

Professionals don't ship first drafts. AI operators don't ship unvalidated outputs.

Step 2 of 4

WHAT WHAT You Need to Know

Quality Gate Architecture

Quality gate types:

Gate Type Mechanism Use When
Automated Rule-based checks Format, length, required elements
AI-Assisted Second AI evaluates first Subjective quality, consistency
Human Person reviews High stakes, nuanced judgment
Statistical Sampling approach High volume, lower stakes

Validation Strategies

1. Checklist Validation Define explicit criteria and verify each:

✓ Contains executive summary
✓ All sections from template present
✓ No placeholder text remaining
✓ Word count within range (800-1200)
✓ No competitor mentions
✓ CTA included
✗ Links verified (needs review)

2. AI Cross-Check Use a second prompt to evaluate the first output:

Review this [content type] against these criteria:
1. [Criterion 1]: Pass/Fail + Explanation
2. [Criterion 2]: Pass/Fail + Explanation
...

Output: JSON with pass/fail for each criterion and overall assessment.

3. Rubric Scoring

The Iteration Loop

When to iterate vs. when to restart:

Situation Action Rationale
80%+ acceptable, minor issues Iterate Refine what's working
50-80% acceptable, structural issues Targeted regenerate Keep good parts, redo bad
<50% acceptable Restart with better prompt Foundation is wrong
Wrong direction entirely Restart with new approach Prompt design failed

Effective iteration prompts:

Issue Iteration Approach
Too long "Condense to X words, preserving [key elements]"
Missing elements "Add [specific element] after [location]"
Wrong tone "Rewrite in [desired tone], maintaining content"
Factual error "[X] is incorrect. The correct information is [Y]. Revise."
Structural problem "Reorganize to follow [structure]: first X, then Y, then Z"

Building Feedback Loops

Feedback capture template:

Field Purpose
Prompt used Reference for refinement
Model/provider Track performance by model
Output quality (1-5) Quantitative tracking
Issues found Specific problems to address
Iterations needed Efficiency metric
Final outcome Did it ship? Why/why not?
Improvement notes What would work better?

Key Concepts

Key Concept

quality gates

Quality gates are checkpoints where output must meet specific criteria before proceeding. For AI workflows, gates typically check:

  • Completeness: Did the AI address all requirements?
  • Accuracy: Are facts and figures correct?
  • Format: Does output match expected structure?
  • Tone: Is the voice appropriate?
  • Constraints: Were restrictions followed?
Key Concept

rubric scoring

Rubric scoring uses defined quality levels (not just pass/fail) to assess output. This enables nuanced evaluation and tracking quality over time.

Example rubric dimensions:

  • Accuracy: 1 (errors) → 5 (verified correct)
  • Completeness: 1 (missing major elements) → 5 (comprehensive)
  • Clarity: 1 (confusing) → 5 (crystal clear)
  • Tone: 1 (inappropriate) → 5 (perfectly matched)
Key Concept

feedback loop

A feedback loop captures information about output quality and uses it to improve future performance. In AI workflows:

  • Input: What was the prompt?
  • Output: What did AI produce?
  • Evaluation: How good was it? (score, pass/fail)
  • Learning: What can improve? (prompt refinement, examples)
Step 3 of 4

HOW HOW to Apply This

Exercise: Design Quality Gates

Quality Metrics to Track

Metric What It Measures Target
First-pass rate % outputs acceptable without iteration 70%+
Iteration count Average revisions needed <2
Human intervention rate % requiring human correction <20%
Quality score trend Rubric scores over time Improving
Rejection rate % outputs not usable <5%
Time to acceptable Duration from prompt to final Decreasing

Common Quality Failures and Fixes

Failure Mode Detection Prevention
Hallucinated facts Cross-reference, fact-check prompt Provide source documents
Incomplete response Checklist validation Explicit requirements in prompt
Wrong tone AI tone check, human spot-check Clear tone examples in prompt
Inconsistent with previous Compare against prior outputs Include relevant context
Template broken Format validation Structured output formats

Self-Check

Practice Exercises

You're building an AI workflow to generate weekly customer intelligence reports. The reports:

  • Summarize key customer feedback themes
  • Highlight urgent issues requiring attention
  • Include sentiment trends
  • Are read by the executive team

Design the quality system:

  1. Define 5+ specific quality criteria (what makes a good report?)

  2. Create a rubric with 1-5 scoring for each criterion

  3. Design validation checkpoints:

    • What can be checked automatically?
    • What needs AI cross-check?
    • What requires human review?

  4. Plan the iteration loop:

    • What issues would trigger iteration?
    • What would trigger full restart?
    • How many iterations maximum before human escalation?

  5. Design feedback capture:

    • What would you log for each report?
    • How would you use this data to improve?
Step 4 of 4

GENERIC Up Next

In Module 2.4: Human-AI Handoffs, you'll learn how to design the boundaries between AI work and human work—the critical transition points where quality and control live.

Module Complete!

You've reached the end of this module. Review the checklist below to ensure you've understood the key concepts.

Progress Checklist

0/5
0% Complete
0/4 Sections
0/3 Concepts
0/1 Exercises