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SciBite / Use Cases / GenAI & LLMs

Large language models and GenAI As organizations continue to search for new ways to unlock better search results, the emergence of retrieval augmented generation (RAG) has provided a framework to improve results. But we believe we can use LLMs, AI and ontologies to go further.

The accuracy and relevance of results produced using an RAG model are ultimately limited by the quality and relevance of the documents that are retrieved during the search process. This means that if your documents aren’t properly annotated and optimized, there’s still a real probability that you will miss vital information. Using ontologies to enrich these documents and support your searches will unlock higher levels of accuracy, relevancy and transparency. 
Why use ontologies with SciBite?
Icon - Enrich your documents
Enrich your documents

Using TERMite from SciBite, you can create highly performant, NER-optimized vocabularies with the power to turn complex documents into machine-readable data. 
Icon - Improve accuracy
Improve accuracy

Raising the quality and relevance of documents with SciBite enables scientists and researchers to identify the correct information on a much more consistent basis. 
Icon - Make new connections
Make new connections

Traditional searches rely on users inputting exact words, phrases and terms. SciBite uses ontologies and AI to add context to reveals results that may have been missed. 
Overview Humans and computers speak different languages. Machines deal in absolutes and understand strings of data, whereas we often operate much more with concepts and ideas.

This can be a big gap, but ontologies form part of the bridge. SciBite uses ontologies to act as translators between these languages.  

By clearly defining terms and how they’re connected, we can enrich documents with the metadata to complement vectorization. This makes it easier for a computer to understand the human’s goal (expressed as a concept or idea) and together with LLMs, deliver enhanced accuracy, improved transparency and greater consistency in information retrieval. 
Advanced matching algorithm

Powered by an advanced matching algorithm, SciBite goes beyond simply selecting overlapping entities to consider the proximity, occurrence and hierarchical relationships between the entities in documents as well as your search terms. 

 Greater transparency

Ontology enrichment enhances transparency by empowering users with detailed information and explanations throughout the document retrieval process.

 More flexibility

SciBite not only enables you to use ontologies to enrich private data with internal vocabularies, but it also allows you to take this terminology and apply it to a range of external data sources too.

 The best of both

Vector-based search can deliver high levels of accuracy in a very effective manner, using this methodology with ontology-enriched documents gives you and your team the chance to heighten the strengths of this method.
Take a closer look

Watch and listen more about how SciBite is using LLMs and AI to take data enrichment and searching to the next level.

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