Quality assurance · Production

Google Cloud powers LLM evaluation service with Labelbox

The problem

Conducting large-scale, high-quality human evaluations of LLMs is a major challenge for enterprises, requiring significant time, resources, and expertise; human evaluation remains the gold standard for understanding nuances but is one of the most time-consuming and resource-intensive parts of the LLM development process.

Workflow diagram · grounded in source
1
Configure evaluation in Vertex AI
trigger
“Vertex AI customers can go directly into the Vertex AI interface to launch an LLM evaluation job, set their desired evaluation type (e.g., single model or side-by-side comparison) and criteria (e.g, question-answer, multi-turn chat, summ…”
2
Labelbox handles pre-QA processing
integration
“With integrated APIs customers can simply configure their task within the Vertex AI platform and everything else is taken care of by Labelbox before the QA process”
3
Human raters evaluate LLM outputs
human_review
“human raters who will help evaluate the effectiveness of their organization's LLMs against a wide range of customizable criteria - from instruction following, verbosity, to relevance of any given response”
4
Results delivered within days
output
“get quality reviewed results within days from skilled evaluation professionals”
5
Customer reviews and accepts outputs
validation
“Seamless visualization of the labeling team's responses within the Vertex AI platform also gives customers the ability to review and accept outputs, putting you in full control of the annotation quality”
Reported outcome

Customers can now develop and ship LLM applications with confidence, receiving high-quality results within days and launching evaluation jobs in minutes.

Reported metrics
LLM evaluation results delivery timewithin days
Time to launch LLM evaluation jobtakes minutes
Reported stack
LabelboxVertex AIBigQueryCloudSQLGoogle Sheets
Source
https://labelbox.com/customers/google-cloud-llm-evaluation
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Customers can now develop and ship LLM applications with confidence, receiving high-quality results within days and launching evaluation jobs in minutes.

What tools did this team use?

Labelbox, Vertex AI, BigQuery, CloudSQL, Google Sheets.

What results were reported?

LLM evaluation results delivery time: within days; Time to launch LLM evaluation job: takes minutes (source-reported, not independently verified).

How is this quality assurance AI workflow structured?

Configure evaluation in Vertex AI → Labelbox handles pre-QA processing → Human raters evaluate LLM outputs → Results delivered within days → Customer reviews and accepts outputs.

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