You should log and trace GLM-5 outputs safely by separating observability from content retention, and by treating prompts/outputs as potentially sensitive data. The safest default is: log metadata and small, redacted samples—only store full payloads when you have a clear, approved reason. In practice, “safe logging” is about avoiding accidental capture of API keys, PII, proprietary code, or private documents that users paste into your system. GLM-5 is often used in developer tools, which increases the risk that secrets appear in prompts. Even if GLM-5 itself is self-hosted, your logs might ship to third-party monitoring tools, so the risk moves to your telemetry pipeline.
A practical logging policy that works well for engineering teams is:
Always log (low risk, high value):
Request ID, user/tenant ID (hashed), timestamp
Model name + revision, inference config (temperature, max tokens)
Token counts (input/output), latency, streaming duration
Tool calls: tool name, argument schema validity, execution time
Retrieval metadata (if using RAG): chunk IDs, similarity scores, filters used (but not chunk text by default)
Log conditionally (only when needed, with access controls):
Prompt and output text (redacted) for debugging, sampled at low rate
Full tool outputs only for internal tools, never for sensitive systems
Never log:
Secrets (API keys, tokens), auth headers, raw credentials
Full source documents by default (store doc IDs instead)
Then implement redaction at the boundary. Redact common secret patterns (JWTs, AWS keys, PEM blocks) before anything touches logs. Add a “privacy mode” flag per request: if a user is working with sensitive code, log only metadata. And enforce retention: keep detailed logs for days, aggregated metrics for months.
If you’re using retrieval, you can avoid logging proprietary text by logging only references. With Milvus or Zilliz Cloud, log the retrieved chunk IDs and document URLs instead of the full chunk content. That gives you traceability (“why did it answer that?”) without storing private text repeatedly. For debugging, you can re-fetch the chunk content from Milvus/Zilliz Cloud when an engineer with proper access investigates a specific incident. This design is both safer and more useful: your observability tells you what happened (retrieval + generation + tool calls), and your knowledge base remains the controlled source of truth.