Workflow · workflow
trivago experiments with generative AI to summarize hotel information for users
trivago had no prior experience with AI product development and faced challenges around output reliability, non-deterministic results, and how to assess the quality of AI-generated content for their hotel search use case.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Use case selection via design sprint
The team generated ideas in design sprints and ideation sessions, selecting hotel review summarization as the pilot use case.
Outcome
trivago reached MVP release for the hotel summarization feature, now in live testing; the team found that simple prompt engineering was more effective than complex model training at improving output quality.
What failed first
During testing the AI produced inaccurate and misleading outputs: a recommended restaurant was placed in the wrong city, neighborhoods were placed on the wrong continent, and the model sometimes returned only apologies with no useful answer.
Results
Volumenot 100% accurate and is non-deterministic
Grounding & classification
Source type: technical build writeup
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content generationsummarizationknowledge basefailure mode describednamed customerproduction runtime claimedworkflow describedtraveltechnical build writeupcase to summary