📄️ 🟢 Introduction
This chapter covers how to make completions more reliable, as well as how to
📄️ 🟢 Prompt Debiasing
This page covers a few simple techniques to debias your prompts.
📄️ 🟡 Prompt Ensembling
Prompt ensembling is the concept of using multiple different prompts to try to
📄️ 🟡 LLM Self Evaluation
Basic self eval
📄️ 🔴 Calibrating LLMs
It is possible to counteract some of the biases LLMs exhibit via calibrating output
📄️ 🟡 Math
Throughout this course, we have seen many different prompting methods that can be used to improve %%LLM|LLM%% math ability. One recent approach, MathPrompter(@imani2023mathprompter), unifies some of these methods (%%CoT|CoT prompting%%, %%PAL|PAL%%, etc.) into a single technique. The overarching idea is to break down a math question into algebraic terms then use Python code to solve it in different ways.