Summary of "Prompt Engineering, RAG, and Fine-tuning: Benefits and When to Use"

- Prompt engineering involves priming a prompt, handling edge cases, ignoring irrelevant information, and formatting the output.

Retrieval augmented generation (RAG) adds dynamic content to prompts by retrieving external knowledge to enhance the model's responses.
To implement RAG, a database with information is needed, text is split and converted into embeddings for retrieval, and the most relevant information is inserted into the prompt.
Fine-tuning involves training a model on prompt completion pairs to teach intuition and improve output quality.
Fine-tuning can optimize speed and cost, narrow the range of possible outputs, and bake in style, tone, and formatting to the model's responses.
Fine-tuning can work together with RAG by training models on examples and improving response quality.
Fine-tuning can be used to create a scalable layer of training data to improve model behavior over time.
Fine-tuning can significantly impact response times and cost, making it a cost-effective option for large volumes of requests.
Prompt engineering, RAG, and fine-tuning all aim to improve model outputs and can be used together as tools for working with large language models.