An LLM observability platform stores prompts, and prompts are the application
An LLM observability platform stores prompts, and prompts are the application A title query for Langfuse returns 346 matches in ZoomEye. The number is small and the contents are unusual. An observability tool for language models records the text that goes into them and the text that comes back, which makes a tracing store closer to a source repository than to a metrics backend. Context and method…
An LLM observability platform records the text inputs and outputs of language models, treating prompts as the application logic. Each language model call generates a trace containing the prompt, model parameters, completion, token counts, latency, and any attached metadata. Traces are sensitive as they hold whatever the application sends, potentially including secrets.
Langfuse indexes these traces, which can include production system prompts, outputs, and embedded secrets. To protect your organization, review all tracing infrastructure as a system of record for application text.
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