# Aisera's Domain Specific LLMs

Aisera’s Generative AI stack includes [fine-tuned LLMs](https://aisera.com/blog/fine-tuning-llms/) that serve specific domains. These [domain-specific LLMs](https://aisera.com/blog/domain-specific-llm/) 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](https://aisera.com/blog/retrieval-augmented-generation-rag/) (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](https://aisera.com/blog/grounding-ai/) 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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