Key Points

1. Prompt engineering is a lot more work than just Googling or ChatGPTing. Make sure the efforts are worth it.

2. Prompt engineering is most beneficial for me when running the same prompt repeatedly or systematically.

3. Test small and then scale. ChatGPT will encourage you until you stop.

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Introduction

Prompt engineering is critical to realizing the full potential of AI models. The clarity, precision, and creativity of our prompts become pivotal. This post documents me dipping my toes into the art and science of prompt engineering, an essential skill set for anyone looking to lead in the AI-driven future.

I started by asking ChatGPT for recommendations on free prompt engineering courses. The three courses ChatGPT recommended are listed below. This blog post provides an example of asking ChatGPT for help learning prompt engineering, not a comprehensive review of tutorials on prompt engineering.

Free Prompt Engineering Courses

DeepLearning.AI + OpenAI — “ChatGPT Prompt Engineering for Developers”

This is a fast course with videos and Jupyter notebooks. You can get through it in 1 to 2 hours.

Platform: DeepLearning.AI

Taught by: Isa Fulford (OpenAI) & Andrew Ng (DeepLearning.AI)

• Focus: Prompting with OpenAI’s models (chat/completion), function calling, zero-shot/few-shot examples.
• Format: Videos + hands-on notebooks
• Duration: ~1 hour
• Very practical and to the point.

Learn Prompting

Platform: learnprompting.org

• Focus: Covers basic to advanced prompt strategies
• Topics: Chain-of-thought, role prompting, fine-tuning intro
• Free, open-source, and continuously updated
• Community-backed with exercises and examples

Prompt Engineering Guide (by DAIR.AI)

Platform: github.com/dair-ai/Prompt-Engineering-Guide

• Focus: Curated tutorials, examples, and papers
• Not a course per se, but an extensive guide with tons of practical content and links
• Ideal for self-paced learners

ChatGPT’s Impromptu Prompt Engineering Tutorial

Additionally, ChatGPT suggested creating a new repository for tutorial Jupyter notebooks and provided the first notebook with basic examples. Then ChatGPT asked about a 2nd more advanced notebook and a 3rd and so on. I saved each notebook as a separate file in a new repo on Github. The files were generated by ChatGPT. They do not run out of the box because they lack full context and are probably dated. Debugging helps me learn. The files are easy to work through for those who learn by doing.

Notes:

• The repository with the ChatGPT tutorial notebooks can be found here.
• Number 07 is just missing.

• This is a tutorial generated on the fly by ChatGPT. It is worth reviewing to see how quickly ChatGPT can generate tutorials and lead users down a path.

  01 - Basics

Prompt engineering is the art and science of crafting effective prompts to interact with AI models, particularly those trained for natural language understanding and generation, such as OpenAI’s GPT series. The process begins with understanding the AI’s capabilities and limitations, and then formulating clear, concise questions or statements to elicit the desired response. This foundational approach ensures that even the most complex AI tools are accessible and productive, catering to specific user needs and enhancing interaction quality.

02 - Chat Format (Experimenting with Roles, Temperature, and Chaining)

In the realm of AI-driven conversations, experimenting with chat formats can significantly alter the interaction dynamics. By adjusting the “temperature” setting, users can control the randomness of responses, with lower values producing more predictable and conservative outputs, and higher values fostering creativity and divergence. Experimenting with roles allows the AI to adopt different personas or expertise levels, enhancing the relevance and engagement of the dialogue. Additionally, chaining prompts involves sequencing multiple queries to build upon previous responses, creating a more fluid and contextually rich exchange.

03 - Advanced Strategies - Personas, Chaining, Tools

Advanced prompt engineering strategies involve creating detailed personas for the AI, enabling it to offer tailored advice, mimic specific communication styles, or assume expert roles in various fields. Effective chaining of prompts, where each query logically follows from the last, supports complex tasks and decision-making processes. Tools like LangChain enhance these capabilities by providing frameworks for integrating AI with other services and databases, thereby expanding the scope and utility of AI applications in real-world scenarios.

04 - Eval Testing

Evaluation testing in prompt engineering is crucial for assessing the effectiveness of AI interactions. This process involves systematic testing of different prompts to determine how well the AI understands and responds to various inputs. By analyzing the accuracy, relevance, and coherence of AI responses, developers can refine prompts to ensure more reliable and contextually appropriate interactions. This iterative testing is essential for tuning AI systems to meet specific user requirements.

05 - Automated Testing

Automated testing in the context of AI involves scripts or programs that automatically run a set of prompts through the AI to evaluate its responses. This method is particularly useful for scaling testing efforts, enabling rapid assessments across numerous scenarios and prompt variations. Automated testing helps in identifying inconsistencies, errors, or biases in AI responses, streamlining the process of improving AI models and their interaction frameworks.

06 - LangChain

LangChain is a tool designed to enhance the capabilities of language models by enabling them to interact with external data sources and APIs. It facilitates the construction of more sophisticated AI applications that can perform tasks ranging from retrieving information and conducting analyses to generating dynamic content based on real-time data. LangChain effectively turns a language model into an integrated part of broader systems, expanding its utility beyond simple question-and-answer formats to more complex and interactive applications.

08 - Parameter Sweeping

Parameter sweeping is a technique used in AI development to find the optimal settings for model parameters. This process involves systematically changing parameters, such as learning rate, batch size, or temperature, to observe how these variations affect the model’s performance. In prompt engineering, parameter sweeping can help determine the best configurations for generating the most effective responses, ensuring that the AI behaves in a predictable and useful manner under various conditions.

09 - Visual Analysis

Visual analysis in prompt engineering involves the use of graphical representations to understand and improve the interactions between humans and AI. This can include plotting response accuracy, sentiment analysis, or other relevant metrics over different prompt types or configurations. By visually analyzing these aspects, developers can quickly identify patterns and anomalies, making it easier to optimize prompt formulations and AI settings to enhance user engagement and satisfaction.

What’s Next

• Build a prompt chain in Python
• Research LangChain and LangGraph for larger applications and building agents