## 📄️ 🟢 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.