RAG Benchmark Athento

Download the complete study on Retrieval Augmented Generation (RAG) at Athento

Athento has conducted a technical benchmark on its RAG (Retrieval Augmented Generation) functionality, combining semantic search and language models (LLMs) to evaluate accuracy, recall, and robustness in real AI document management environments.

This study shows how Athento’s AI-powered ECM platform enables natural language queries to be answered with accurate information based on corporate documentation.

Discover the results of the Athento RAG Benchmark and how AI performs in real document management environments.

Fill out the form and access the RAG Benchmark Athento now

In accordance with Article 13 of the GDPR and Article 11 of the LOPDGDD, your personal data will be processed by ATHENTO EUROPA S.L. for the purpose of responding to user queries, with consent being our legal basis for processing your personal data. We will only process the data you provide us with, and it may be transferred to third parties in order to fulfill the purpose, such as ActiveCampaig or Open AI. There are automations through Open AI, however, there is intervention, so these are not automated decisions. You can exercise your rights of access, rectification, opposition, deletion, portability, and limitation of your personal data by sending an email to: dpo@athento.com. You can consult additional and detailed information on Data Protection in our Privacy Policy.

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Don't miss this free resource on RAG assessment in business document management

Download the RAG Benchmark Athento for free and discover how Retrieval-Augmented Generation (RAG) enables accurate and reliable responses to complex queries based on corporate documentation.

In this benchmark, we analyze Athento’s functionality across more than 1,700 documentation items, evaluating accuracy, recall, and robustness so you can see firsthand how RAG performs in a real-world environment.

With this Benchmark, you will learn:

How Athento combines semantic search with FAISS and language models (LLMs) to deliver reliable answers.
How to configure a business knowledge base to maximize accuracy and robustness in queries.
What performance metrics (accuracy, recall, and robustness) can be expected and how the results are interpreted.
What factors influence the quality of responses, from documentation to the formulation of queries.

RAG Benchmark Athento

Download the complete study on Retrieval Augmented Generation (RAG) at Athento

Fill out the form and access the RAG Benchmark Athento now

Don't miss this free resource on RAG assessment in business document management

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