Start building even more reliable AI applications by equipping LLMs with accurate and relevant knowledge. This will greatly improve their knowledge and thus reduce the likelihood of hallucinations.

Why use a Knowledge Base?

The primary purpose of a knowledge base and a Retrieval-Augmented Generation (RAG) is to provide a reliable source of information that an LLM can access. By querying a knowledge base, an LLM can retrieve relevant data to answer questions or solve problems more accurately. This integration ensures that the information provided is both relevant and precise, enhancing the overall effectiveness of the model.

Use cases

1. Reduced Hallucination: By relying on a well-structured knowledge base, the likelihood of the LLM generating incorrect or fabricated information (hallucinations) is significantly decreased.
2. Specific Context: A knowledge base allows for the inclusion of domain-specific or company-specific information, ensuring that the responses generated by the LLM are more aligned with the intended context.
3. Up-to-Date Information: Unlike static models, a knowledge base can be continuously updated with the latest information, providing the LLM with current and accurate data.

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Read more about Knowledge Bases in our docs