Recent joke I heard goes “ChatGPT is like a corporate consultant who knows nothing about your business.” This analogy rings true for all general-purpose Large Language Models (LLMs). Although LLMs come preloaded with a vast public-domain knowledge, they are not specifically tailored to unique knowledge about your enterprise use cases to solve your problems. It’s crucial to “prepare” the LLM within the specific context and data of your use case to prevent it from making erroneous assumptions, often referred to as hallucinations.On other hand adding your high-value data sets into LLM is “non-returnable” process. How to overcome this dilemma of adding your business context in GenAI without losing the high-value private data assets?

Join the session to get deep insights into:

• Dilemma of adding enterprise data assets into GenAI vs Data asset control
• Two common approaches Fine-Tuning & RAG
• Architectural blueprints for End-to-End adoption of GenAI for enterprises