Assessment

AI Performance Assessment for Large Language Models

Prove that the LLM you have adapted can be trusted.

An independent technical testing service that evaluates the performance and outputs of large language model (LLM) implementations against defined, measurable criteria

LLMs are transforming the way organizations interact with customers, automate workflows and generate insights. However, as adoption increases, so does the need for objective evidence that AI outputs are accurate, relevant, safe, and reliable.

BSI's AI Performance for Large Language Models service provides independent technical testing to help organizations understand how their implemented LLM performs against clearly defined, measurable criteria.

By identifying strengths, highlighting potential risks, and validating model performance in realistic use cases, the service helps you build confidence in your AI systems and demonstrate they are fit for purpose.

Why choose BSI for your AI Performance Assessment for LLMs

Builds credibility through trusted third-party AI verification.
Global teams of AI experts.
Triple accredited Certification Body for ISO/IEC 42001 (ANAB, RvA, UKAS).
We are a global organization, trusted and recognized around the world.

FAQs

Frequently Asked Questions about our AI Performance Assessment LLM

The service independently evaluates the quality of your implemented LLM's outputs against agreed performance criteria. Testing can assess areas such as answer correctness, factual consistency, context relevance, toxicity and summarization quality, providing objective evidence of how your model performs in realistic scenarios and whether it is fit for its intended purpose.
The service is designed for organizations that build, deploy or customize large language models. This includes providers of chatbots and retrieval-augmented generation (RAG) solutions, as well as enterprise teams that want independent evidence of model performance before deployment, procurement or wider business adoption.
You'll receive an independent evaluation of your implemented LLM's performance against the agreed testing criteria, including clear results, evidence to support internal and external assurance activities, and insights into your model's strengths and areas for improvement. The findings can help support deployment decisions and build confidence with customers, partners and stakeholders.
Independent testing helps reduce the risks associated with deploying LLMs by identifying issues such as inaccurate responses, hallucinations, irrelevant content or unsafe language before they affect users. It also provides credible third-party evidence that can support governance, procurement, compliance and customer assurance requirements.
This service is focused on large language model applications, including chatbots, retrieval-augmented generation (RAG) systems and summarization tools. It evaluates the quality and performance of LLM outputs and is not intended for other AI technologies such as computer vision, image processing, forecasting or recommendation systems.