You build a chatbot that uses the Azure OpenAI GPT 3.5 model. You need to improve the quality of the responses from the chatbot. The solution must minimize development effort. What are two ways to…

Fine-tuning the model involves significant data preparation and computational resources, which does not minimize development effort.

Providing grounding content, often through Retrieval Augmented Generation (RAG), improves response quality by supplying the model with relevant external information, which helps anchor its answers to specific, accurate data without modifying the model itself.

Adding sample request/response pairs, also known as few-shot learning or in-context learning, guides the model to produce desired outputs by showing it examples within the prompt, which is a low-effort way to steer its behavior without retraining.

Retraining a language model with your own data is an extensive and resource-intensive process, going against the requirement to minimize development effort.

Training a custom large language model (LLM) from scratch is a highly complex, time-consuming, and resource-intensive endeavor, far from minimizing development effort.