Making Large Language Models and Retrieval-Augmented Generation Work With Ease (Presented by Softserve, Inc.)
, AVP of AI and Data Science, Softserve, Inc.
Large language models (LLMs) provide new possibilities for engaging and intelligent conversational systems. However, productionizing and managing these models and ensuring they work to your advantage can be challenging. Two key strategies that can help are RAG-workflows and NeMo Guardrails.
Retrieval-augmented generation (RAG) is a powerful technique that combines the strengths of retrieval-based models and generative models by leveraging customers' proprietary data. This innovative workflow leverages the LLM for inference by providing a prompt with additional context, significantly enhancing the performance of LLMs and enabling them to handle a wide range of tasks with greater flexibility and capability.
NeMo Guardrails provides a comprehensive framework for seamlessly integrating programmable guardrails into LLM-based conversational systems. These guardrails serve as protective measures, effectively mitigating potential LLM-based attacks, ensuring a trustworthy and secure conversational system and addressing LLM issues like hallucinations.