Skill.reFeedback Loops: How to Report AI Errors and Improve System Performance
Overview
Lecture URL: https://skill.re/learn/recruiting/feedback-loops-how-to-report-ai-errors-and-improve-system-performance.php
TRANSCRIPT: Feedback Loops: How to Report AI Errors and Improve System Performance
Course: AI for Recruiters - Professional Credential
Module: Level 2: Hands-On Foundations
Section: Chapter 10 -- Guardrails and Supervised Use
Theme: guardrails-and-supervised-use
Lecture: 10.4
Duration: 60 min
Format: Workshop + Hands-On
Audience: Recruiters beginning to use AI tools
Prerequisites: L1 Certification
What you will learn: You'll create systems for reporting AI errors and collecting feedback that
helps improve AI tool performance over time through structured improvement cycles.
INTRODUCTION
AI doesn't improve by accident. It improves through feedback. When you use AI, you learn things
about how it works, where it fails, what it does well. That learning should feed back into
improving your prompts, your processes, and (through vendors) the tools themselves.
Today, we're building feedback systems that create continuous improvement.
THE FEEDBACK LOOP SYSTEM
A good feedback system has four steps: capture, categorize, analyze, act.
STEP 1: CAPTURE
Every time you find an AI error or issue, capture it:
WHAT WENT WRONG: Be specific. "Hallucinated years of experience" not "bad summary."
WHERE IT HAPPENED: In screening? Summarization? Outreach? Note the context.
EVIDENCE: What was the error? What was correct? What was the impact?
SEVERITY: Critical (affects decision), major (notable issue), minor (doesn't affect much)
Capture tool: Could be a form, a spreadsheet, a shared document, or even a Slack thread if
coordinating with team.
STEP 2: CATEGORIZE
Group errors by type:
- Hallucinations (made-up facts)
- Misinterpretations (wrong meaning)
- Omissions (missing information)
- Bias (discriminatory language or assumptions)
- Other issues
This categorization helps you see patterns.
STEP 3: ANALYZE
Look at patterns across errors:
- What's the most common error type?
- When does it happen? (Certain roles? Certain tools? Certain prompts?)
- What's the root cause?
- What's the impact?
STEP 4: ACT
For each pattern, take action:
- Improve prompts to prevent the error
- Add constraints to your process
- Change tools if vendor issues are unfixable
- Train team on the issue
- Escalate if it's a legal/compliance issue
FEEDBACK SOURCES
Feedback comes from multiple places:
SOURCE 1: YOUR OWN USE
You use AI, notice errors, log them. This is your primary feedback source.
SOURCE 2: TEAM FEEDBACK
If your team uses AI, they see issues too. Create a channel for them to report.
SOURCE 3: CANDIDATES
Sometimes candidates will tell you AI made errors ("Your email said I did X, but I didn't"). Listen.
SOURCE 4: OUTCOMES
Track: Did candidates you screened in AI work out? Did candidates screened out fit elsewhere?
Misses and false positives are feedback.
SOURCE 5: FORMAL AUDITS
Periodically audit your AI-assisted decisions systematically. Look for patterns of error or bias.
CREATING A FEEDBACK CULTURE
For feedback systems to work, your team must see them as useful, not punitive.
CULTURE PRINCIPLE 1: FEEDBACK IS IMPROVEMENT, NOT BLAME
Frame it: "We found an error. How do we fix it?" not "Someone made a mistake."
CULTURE PRINCIPLE 2: MAKE REPORTING EASY
Don't create bureaucracy. Make it as easy as possible to report issues.
CULTURE PRINCIPLE 3: CLOSE THE LOOP
When someone reports an issue, tell them: How will this be fixed? What's the timeline? Did it
work?
CULTURE PRINCIPLE 4: CELEBRATE LEARNING
When someone catches an AI error that would have caused a problem, that's a win. Acknowledge it.
FEEDBACK TEMPLATES
Make it easy to capture feedback:
SIMPLE FORM:
Or even simpler:
ANTI-PATTERNS
ANTI-PATTERN 1: NO FEEDBACK SYSTEM
Description: Errors happen, but they're not captured or analyzed.
Why it fails: You keep making the same mistakes. You don't improve.
How to avoid: Build a feedback system upfront.
ANTI-PATTERN 2: FEEDBACK WITHOUT ACTION
Description: You collect feedback but don't do anything with it.
Why it fails: Team stops reporting. System dies.
How to avoid: Close the loop. Act on feedback. Tell people what happened.
ANTI-PATTERN 3: BLAME CULTURE
Description: Reporting errors feels risky because it might reflect poorly on you.
Why it fails: People hide errors instead of reporting them.
How to avoid: Frame feedback as improvement, not blame.
PRACTICE PROMPTS
Exercise 1: Design Your Feedback System
What tool will you use? What information will you capture? Who reports? How often do you analyze?
Exercise 2: Create Your Feedback Template
Draft a simple form for capturing AI errors.
Exercise 3: Run a Feedback Cycle
Use AI for a task. Intentionally capture any errors. Categorize them. Analyze for patterns.
Exercise 4: Identify Improvement Actions
From your analysis, what would you change? Prompts? Process? Tool selection?
Exercise 5: Test Your Process
Use the improved prompts or process. Did it reduce errors?
KEY TAKEAWAYS
- Feedback loops create continuous improvement: capture -> categorize -> analyze -> act.
- Capture: specific errors with context and impact.
- Categorize: group by error type to see patterns.
- Analyze: what's the root cause? When does it happen? What's the impact?
- Act: improve prompts, add constraints, change processes, escalate if needed.
- Create a culture where feedback is seen as improvement, not blame.
- Close the loop. Tell people what you did with their feedback.
GLOSSARY
Feedback Loop: The cycle of capturing errors, analyzing patterns, and acting to prevent them.
Capture: Recording specific errors and context.
Categorize: Grouping errors by type to identify patterns.
Root Cause: The underlying reason an error occurred.
Improvement Action: Changes to prompts, process, or tools based on feedback.
SYNTHESIS AND APPLICATION
Every error is an opportunity to improve. When you capture and act on feedback systematically, your
AI use becomes more reliable and effective over time. That's how organizations with mature AI
practices outpace those that don't.
This week, build your feedback system. Start capturing errors. Analyze for patterns. Take one
improvement action.
REFLECTION EXERCISE
- What errors have you seen most often in your AI use? What's the pattern?
- If you had a feedback system, what would you learn about your AI use?
- How would your team respond to being asked to report AI errors?
- What would change about your process if you committed to one improvement action per month based
on feedback?
CLOSING REMARKS
AI improves through feedback. Responsible organizations build feedback systems and act on them.
In our final session, we're bringing this all together: building team agreements that embed
responsible AI practices into your culture.
AI for Recruiters Certification Program
Level 2: Hands-On Foundations | Guardrails and Supervised Use | Lecture 10.4
A SkillsClinic initiative.
Duration: ~60 minutes | Word Count: ~2,420