Summary

Completed

In this module, you learned how to optimize generative AI model performance using complementary strategies in Microsoft Foundry.

You learned how to:

• Apply prompt engineering techniques including system messages, few-shot learning, and model parameters to optimize model output.
• Understand when and how to ground a language model using Retrieval Augmented Generation (RAG).
• Identify when fine-tuning a model improves behavioral consistency.
• Compare optimization strategies and determine when to combine them.

The key takeaway is that prompt engineering, RAG, and fine-tuning aren't competing approaches—they're complementary strategies that address different dimensions of model performance. Start with prompt engineering to guide the model's behavior, add RAG when factual accuracy requires domain-specific data, and consider fine-tuning when you need consistent style and format that prompt engineering alone can't reliably achieve.

For the travel agency scenario, the most effective solution might combine all three: a fine-tuned model that maintains the brand voice, RAG that grounds responses in the actual hotel catalog, and prompt engineering that adds conversation-specific instructions and safety guardrails.

Learn more

• Getting started with customizing a large language model (LLM)
• Prompt engineering techniques
• System message design
• Retrieval Augmented Generation in Microsoft Foundry
• Customize a model with fine-tuning
• Microsoft Foundry fine-tuning considerations
• Augment large language models with RAG or fine-tuning