Enterprises are racing to harness the power of generative AI, but must choose how to tailor large language models (LLMs) to their needs. Two key methods are Retrieval-Augmented Generation (RAG) and fine-tuning . Both enrich a general-purpose model for specific tasks, but in different ways. RAG links an LLM to a company’s own data at query time, while fine-tuning further trains the model on targeted data sets. In simple terms, RAG lets an AI “look things up” from an up-to-dat

Bank and credit union customers today expect more than quick responses. They expect accuracy, personalization, and a seamless experience across every touchpoint. Enter GenAI agents: LLM-powered assistants that can handle everything from balance checks to fraud alerts with context, speed, and a touch of intelligence.

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Retrieval-Augmented Generation (RAG) is a technique that can be used to feed enterprise-specific knowledge to a Large Language Model (LLM)

Small Language Models (SLMs) are quickly emerging as a practical alternative to their larger counterparts, offering plenty of benefits.

Through Function Calling, Azure OpenAI allows you to use AI and APIs to create richer, seamless experiences.

Function Calling is a GPT feature used to feed the model dynamic data. This can be used as the context to generate relevant content.