so i’ve been experimenting with a small AI-based project recently and started thinking about logging around prompts, responses, and model calls etc etc.
for traditional systems observability tools handle most of this, but with LLM-based apps it feels less clear what the standard approach is, especially if you need proper audit trails for debugging or compliance.
curious how teams are handling this in production
are people mostly building their own logging pipelines, or are there reliable tools/platforms that help with storing and auditing LLM interactions?
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Ask HN: Best way to implement logging and audit trails for AI apps? · HackerTrans
for traditional systems observability tools handle most of this, but with LLM-based apps it feels less clear what the standard approach is, especially if you need proper audit trails for debugging or compliance.
curious how teams are handling this in production
are people mostly building their own logging pipelines, or are there reliable tools/platforms that help with storing and auditing LLM interactions?