Introduction

Imagine having a conversation with a genie who can grant your wishes. The only catch? Your wishes—or in this case, your prompts—need to be carefully worded to unlock the genie’s full potential.

Prompt engineering significantly enhances the capabilities of large language models (LLMs) with minimal effort. With a well-crafted prompt engineering toolkit, you can tailor LLM behaviour to tackle a wide range of tasks, unlocking their full potential regardless of their size.

In this article, we will interact with and prompt engineer LLaMA-2 model to analyze documents, generate text, and be an AI assistant. By the time you complete this article you will be able to:

• Iteratively write precise prompts to bring LLM behaviour in line with your intentions.
• Leverage editing the powerful system message.
• Guide LLMs with one-to-many shot prompt engineering.
• Incorporate prompt-response history into the LLM context to create chatbot behaviour.

Prerequisites

This article is written for Python developers who have used ChatGPT or similar large language models but haven't yet worked with LLMs programmatically.

While familiarity with the fundamentals of large language models is helpful, it's not essential to benefit from this article.

We take a hands-on approach, focusing on practical development rather than theory. When theoretical concepts are necessary, we cover them concisely.

🔮 Iterative Prompt Development

Let's begin by working through a series of simple prompts, getting familiar with the transformers pipeline and the LLaMA-2 model that we'll use throughout this article.

By iterative experimentation with simple prompts, we'll explore three key concepts: creating specific prompts, using helpful cues, and giving the model "time to think" when tackling complex tasks.

Create LLaMA-2 Pipeline

**from** transformers **import** pipeline

model **=** "TheBloke/Llama-2-13B-chat-GPTQ"

llama_pipe **=** pipeline("text-generation", model**=**model, device_map**=**"auto");

Helper Function

We will use the following function to support our interaction with the LLM.