Notes from the Prompting Lab

Here at Libretto, we're trying to turn prompt tinkering into prompt engineering. This means we do a lot of empirical experiments on what works and what's just hype when it comes to prompt engineering, and here on our blog is where we share what we've learned.

Prompt Engineering Should Be Fast, Automated, & Fully Empirical — Here’s Libretto’s Approach

When looking at in-context learning, should you use 1-shot, 3-shot, 5-shot, 9-shot?

How many few shot examples should you use?

If a set of few-shot examples works with one model, will they work for another model?

Are the best few-shot examples applicable across models?

How much does it matter which few-shot examples you choose to put in your prompt? We dig in to the details!

Notes from the Prompting Lab

Prompt Engineering Should Be Fast, Automated, & Fully Empirical — Here’s Libretto’s Approach

How many few shot examples should you use?

Are the best few-shot examples applicable across models?

Does it matter which examples you choose for few-shot prompting?