Langfuse gives teams evidence about how an LLM feature behaved through traces, prompts, evaluations, datasets, and the sessions around them. Strawberry can turn the project view and selected supporting material into a concrete incident brief, quality review, or prompt-change investigation that keeps the path from observed trace to proposed fix visible.
That is more useful than a generic model summary because the team can see which evidence supports a conclusion and which gap still needs testing.
01
Follow a quality issue back to the trace.
An LLM complaint is hard to repair when the team has only a screenshot or a loose description of the failure. Strawberry can organise the visible Langfuse trace, session, prompt, and evaluation context into an incident narrative with evidence attached.
02
Compare prompt changes with evaluation evidence.
A new prompt version should not be called better because one example reads better.
Bring its evaluation output, requirement, and problematic cases into the same review before a team changes direction.
03
Make the next test more specific than the last summary.
A useful investigation ends with a falsifiable validation step, not “monitor this.” Strawberry can turn trace patterns into a proposed test that names the expected behaviour, dataset or scenario, and evidence that would settle the question.
04
Bring fresh quality signals to the team before the week fills up.
A Monday 09:30 review places new evaluation failures and prompt changes in the planning conversation while owners can still set testing priorities. Save the project’s own score thresholds and critical scenarios in a Langfuse skill so the routine creates a relevant assurance packet rather than a trace dump.
Langfuse Strawberry