With the previous lesson, we have seen how Generative AI is changing the technology landscape, how Large Language Models (LLMs) work and how a business - like our startup - can apply them to their use cases and grow! In this chapter, we’re looking to compare and contrast different types of large language models (LLMs) to understand their pros and cons.
The next step in our startup’s journey is exploring the current landscape of LLMs and understanding which are suitable for our use case.
2.1 Introduction
2.2 Learning Goals
2.3 Understand Different Types of LLMs
2.4 Foundation Models versus LLMs
2.5 Open Source versus Proprietary Models
2.6 Embedding versus Image generation versus Text and Code generation
2.7 Encoder-Decoder versus Decoder-only
2.8 Service versus Model
2.9 How to test and iterate with different models to understand performance on Azure
2.10 Improving LLM results
Knowledge check
What could be a good approach to improve LLM completion results?
- Prompt engineering with context
- RAG
- Fine-tuned model
A:3, if you have the time and resources and high quality data, fine-tuning is the better option to stay up to date. However, if you’re looking at improving things and you’re lacking time it’s worth considering RAG first.
Challenge
Read up more on how you can use RAG for your business.