SUVA, by harnessing the power of retrieval augmented generation, machine learning, NLP, NLQA, generative AI and an insights engine helps resolve customer and employee support queries 24/7 — in the most contextual, personalized, intent-driven manner — with the least amount of user effort.
Organizations are in a race to adopt Large Language Models. And while, organizations stand to gain a lot of productivity improvements through LLMs, when a user question is directly sent to the open-source LLM, there is increased potential for hallucinated responses based on the generic dataset the LLM was trained on.
This is where SUVA’s Retrieval Augmented Generation approach to LLMs comes into play. By enhancing the user input with context retrieved from a 360-degree view of the enterprise knowledge base, the LLM-integrated SUVA can more readily generate a contextual response with factual content.
With SUVA’s support for leading LLMs including Hugging Face™, BARD, Open AI™ and more, you can easily kickstart leveraging SUVA’s LLM-infused capabilities with a plug and play integration by just inputting the API keys for your LLM.
With SUVA, an easy to use UI setting ensures admins get access to temperature control, which is a parameter used in chatbot interactions to adjust the randomness and creativity of the responses generated by the model. It affects the level of probabilistic uncertainty and variability in the virtual assistant's outputs.
By auditing and filtering out queries that are transactional in nature, SUVA segregates intents and stores queries which get multiple hits by caching, thus preventing LLM query hits for repeat queries, hence reducing LLM usage costs.
Instead of following a fixed, predefined path, the virtual assistant uses the language model to determine the appropriate next step based on the user's query, allowing for a more flexible and adaptive conversation.
SUVA respects role-based access controls to define user roles and associate them with specific access privileges when it comes to responses. By continuously adapting based on user interactions, SUVA analyzes, engagement patterns, and preferences to improve interactions.
In situations where the LLM in question is inaccessible, the fallback mechanism allows SUVA to provide alternative responses or handle user queries appropriately, from your index (stored in a knowledge base or a separate fallback module).
