AI STRATEGY
Monitor, Adapt, and Respond Responsibly
Track What the AI Says and Does
Logging is the foundation for all responsible AI practices. Capturing every model input, output, and user interaction lets you diagnose issues, track changes, and drive continuous improvement.
Why it's Important
Creates a record for debugging and analysis
Enables performance comparison between versions
Supports compliance, especially in regulated industries
Detects drift, regressions, and emerging issues
Increases visibility for cross-functional teams
How to Implement
Log prompts, completions, feedback, edits, and session metadata
Include model version, prompt ID, user type, and timestamp
Anonymize data to protect privacy and comply with laws
Store logs in a structured, queryable format (e.g., BigQuery, Snowflake)
Set retention policies and access controls
Make logs accessible for product, data, and QA teams
Visualize logs with BI dashboards or open-source tools
Available Workshops
Logging Schema Design Sprint
Privacy Impact Review
QA Log Review Drill
Multi-Team Log Access Mapping
Logging Infrastructure Planning
Data Labeling for Log Analysis
Deliverables
Logging spec document
Sample log output files
Anonymization policy
Access control matrix
Logging dashboard prototype
How to Measure
% of interactions successfully logged
Log error or dropout rate
Query latency for log retrieval
Team access adoption metrics
Compliance audit pass/fail outcomes
Volume of logs used in tuning/QA each month
Pro Tips
Use logs to build real-world gold test sets
Map logs to feedback for combined insight
Add log triggers to alert on anomalies
Use log review in sprint retrospectives
Automate log integrity checks
Get It Right
Start small—log only what matters most first
Include metadata for versioning and traceability
Use logs as part of model postmortems
Ensure log consistency across environments
Educate teams on how to use logs
Don't Make These Mistakes
Logging sensitive data without redaction
Letting logs become inaccessible or siloed
Collecting too little context to be useful
Logging inconsistently across user paths
Failing to define data governance for logs