Domain Specific LLMs
Aisera’s Generative AI stack includes fine-tuned LLMs that serve specific domains. These domain-specific LLMs use open-source models and proprietary datasets, and they master domain knowledge so that they can reason upon enterprise data to deliver fast, accurate, and consistent responses to user requests.
LLM fine-tuning combines domain specificity and task specificity. Aisera’s LLMs have been fine-tuned using datasets in the following domains: IT, HR, Financial Services, Banking, Insurance, Clinical Trial Ops, more. For each domain, fine-tuned models are trained to handle domain adaptation tasks such as summarization, domain classification, document validation for answering queries with Retrieval Augmented Generation (RAG), Next Best Action suggestions, and more.
Using domain and task-specific LLMs rather than a general-purpose language model brings significant benefits in the model’s accuracy, reduces latency and ensures compliance with privacy requirements for customer data. See more details below:
Deeper knowledge of complex domain-specific language: The model is adapted to have a deeper understanding of domain-specific vocabulary and the nuanced meanings of frequently used terms in the domain. The model’s responses are contextually aware and accurate.
Lower latency and cost: Domain-specific LLMs are able to perform tasks with shorter prompts. Response time is reduced to meet the customer service automation standards while computing cost is also reduced at inference.
Data Privacy with a TRAPS Framework: We deploy responsible Generative AI apps with Aisera’s TRAPS Framework (Trusted, Responsible, Auditable, Private & Secure) while ensuring privacy compliance with PII anonymization. Add more.
Aisera’s domain-specific LLMs are coupled with domain-specific ontologies and knowledge graphs, such as classes of entities that capture domain and custom knowledge. The ontologies and knowledge graphs are leveraged to improve AI Search and RAG results grounding the model’s responses and eliminating hallucinations. The LLM is aware of domain and customer-specific entities and their relationships and uses them to complete complex tasks. This results in a further accuracy boost ensuring that the assistant’s responses meet the requirements at enterprise level.
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