Deploying Large Language Models With Retrieval Augmented Generation

Understand

Knowing that the generative capabilities of large language models (LLM) are sometimes hampered by tendencies to hallucinate or create non-factual responses, researchers have increasingly focused on methods to ground generated outputs in factual data.

• Retrieval Augmented Generation (RAG) has emerged as a key approach for integrating knowledge from data sources outside of the LLM's training set, including proprietary and up-to-date information.
• While many research papers explore various RAG strategies, their true efficacy is tested in real-world applications with actual data.
• The journey from conceiving an idea to actualizing it in the real world is a lengthy process.