This session discusses the three main approaches to using large language models (LLMs): training, fine-tuning, and prompt engineering. Training is the most time-consuming and expensive approach, but it can lead to the best performance. Fine-tuning is less time-consuming and expensive, but it can still lead to significant improvements in performance. Prompt engineering is the least time-consuming and expensive approach, but it can be less reliable.
Key Takeaways:
- Training:
- Training LLMs requires a large dataset of text and code.
- Training can take weeks or months, depending on the size of the dataset and the model architecture.
- Training can be expensive, depending on the hardware used.
- Training can lead to the best performance, but it is not always necessary.
- Training LLMs requires a large dataset of text and code.
- Fine-tuning:
- Fine-tuning LLMs requires a small dataset of labeled data.
- Fine-tuning can take hours or days, depending on the size of the dataset and the model architecture.
- Fine-tuning is less expensive than training, but it can still be costly.
- Fine-tuning can lead to significant improvements in performance, but it is not always necessary.
- Fine-tuning LLMs requires a small dataset of labeled data.
- Prompt Engineering:
- Prompt engineering does not require any training data.
- Prompt engineering can be done quickly and easily.
- Prompt engineering is the least expensive approach to using LLMs.
- Prompt engineering can be less reliable than training or fine-tuning.
- Prompt engineering does not require any training data.
This session concludes by discussing the trade-offs between the three approaches to using LLMs. The best approach depends on the specific task at hand and the resources available.
