Prompt engineering is a discipline that stands at the intersection of human ingenuity and artificial intelligence. It’s growing rapidly not because it’s trendy but because it’s necessary. If you want to work with AI efficiently, you must learn the basics of prompt engineering.
What is Prompt Engineering?
Prompt engineering emerged from the rapid advancement of artificial intelligence. It involves crafting, refining, and optimizing prompts to enhance the interaction between humans and AI. This requires a strong understanding of AI’s capabilities and limitations and a continuous effort to keep the prompt library fresh and effective.
At its core, artificial intelligence simulates human intelligence processes through machines, leveraging vast amounts of data to identify patterns and predict outcomes. It’s not creative. It can’t think for itself. It’s machine-learning and tools like ChatGPT and text-to-image models like MidJourney have made it easy for even non-technical people to use AI.
Why Prompt Engineering?
The almost unprecedented growth of AI has made prompt engineering an essential skill. As AI evolves, the way we communicate with it must also adapt to ensure clear, effective, and accurate interactions.
When a normal user sits down and interacts with AI, he or she types in a few sentences to explain what the AI tool must do. Then the AI chat dance begins. This dance could take 10–15 minutes of reviewing AI responses, asking more specific questions, fine-tuning, or explaining in greater detail what the expectations are until the desired results appear on the screen.
A good prompt chain that was engineered by a professional with a firm understanding of the AI in question can cut all that time and back-and-forth to two minutes.
And that’s why stellar prompt engineers can command high salaries.
The Limitation of Large Language Models (LLMs)
AI is remarkable. It can do things that previously required human intelligence like decision-making, problem-solving, understanding language, and more. Large Language Models (LLMs), like Co-Pilot, Gemini, and ChatGPT, are advanced AI systems designed to understand and generate human-like text based on the input they receive. They’re trained on vast amounts of text data so they learn the nuances of language and respond in a way that mimics human conversation.
And that can be part of the problem for many people. For those who don’t understand how the technology works, AI can seem very human but also, very infallible. LLM models are 100% dependent on the data they were trained on, and they have no ability to extend their knowledge base beyond that. I can better explain this limitation with one simple prompt example…
First, pull out your phone and look at the weather. What’s the temperature outside right now?
Now, go to ChatGPT and enter this prompt, “What’s the weather like in [add your city] today?”. The answer ChatGPT gives you won’t match with your phone which tells you about the weather in real-time. The reality is that AI doesn’t know real-time weather; instead, it generates a response based on patterns it has learned during training. So, it takes the average temperature for your city at the time that you asked, and it creates an average temperature based on the last 5 years or more of historical weather data.
Meaning if your city just got hit with a very surprising and unusual cold front, and you’re drowning in five feet of snow, ChatGPT has no idea.
Best Practices for Prompt Engineering
Prompt engineering is the art of crafting queries that guide AI to produce the most effective and relevant responses. Here are some best practices:
- Be Specific in Your Queries: The more specific your prompt, the more accurate and relevant the AI’s response will be.
Example: Instead of asking, “How do I cook meat?” specify the type of meat and cooking method, e.g., “How do I grill a medium-rare steak on a propane grill?”
- Adopt a Persona: This involves crafting prompts as if you were a particular character, which helps tailor the AI’s responses to fit a specific narrative or audience.
Example: “Write a birthday greeting from a pirate to his parrot.”
- Employ Iterative Prompting: If the initial response isn’t sufficient, refine the prompt based on the AI’s output to get closer to the desired answer.
Example: Start with “Describe the steps to bake bread,” and follow up with, “What temperature should the oven be for baking bread?”
Zero-shot and Few-shot Prompting
These techniques involve querying AI models without any or with minimal prior examples:
- Zero-shot prompting: The model generates a response based on a single prompt without any specific training on that task. This can also be helpful for brainstorming purposes when you don’t have a clear idea of what you want or need.
Example: Asking your AI of choice, “Explain the theory of relativity,” without prior examples.
- Few-shot prompting: This involves providing the model with a few examples to guide its responses, helping it better understand the context or task.
Example: Showing the AI three different ways to start an email before asking it to draft one.
Dealing with AI Hallucinations
AI hallucinations occur when models generate incorrect or nonsensical responses. Hallucinations are just friendly developer-speak for mistakes. Understanding why these errors occur can help in writing better prompts and correcting outputs.
Here’s the most common way hallucinations (errors) happen in AI:
If an AI describes a historical event inaccurately, like claiming “The Eiffel Tower was built in 1923,” it’s likely due to overfitting on incorrect data during training. Refining the prompt or providing correct historical context can help mitigate this.
Keep in mind that all LLMs were trained on incredible amounts of data from the web and, as you already know, not all the info online is accurate! For two days, before it was fixed, the Eiffel Tower’s Wikipedia page said it was built in 1923. If the LLM analyzed that Wikipedia page over those two days, it now thinks that the Eiffel Tower was built in 1923 as well. Note: The Tower was built in 1889. And you have a “hallucination”!
10 Examples: Standard vs. Expertly Engineered AI Prompts
Who doesn’t learn faster with examples? Even AI does! Below are some regular AI requests with one small variation; the first request is the standard prompt used by most people, the second is the refined prompt used by an engineering expert.
Sure, it may seem unnecessary to use an expert engineered prompt to request the weather, but you’ll see the difference. More carefully designed prompts can significantly improve the specificity and relevance of AI responses, try them out for yourself in any AI chat:
- Weather Inquiry
- Standard Prompt: “What’s the weather like?”
- Expert Engineered Prompt: “What’s the forecast for tomorrow afternoon in Seattle?”
- Cooking Instructions
- Standard Prompt: “How to make pasta?”
- Expert Engineered Prompt: “Provide a step-by-step guide for making homemade fettuccine Alfredo for two.”
- Travel Recommendations
- Standard Prompt: “Good places to visit in Europe?”
- Expert Engineered Prompt: “List the top 5 family-friendly attractions in Paris with brief descriptions and best visiting months.”
- Language Learning
- Standard Prompt: “Teach me Spanish.”
- Expert Engineered Prompt: “Create a 7-day beginner’s schedule for learning Spanish, focusing on essential phrases and daily practice activities.”
- Fitness Advice
- Standard Prompt: “How to lose weight?”
- Expert Engineered Prompt: “Provide a 4-week exercise plan for someone looking to lose 10 pounds, including dietary suggestions and daily workout routines.”
- Tech Support
- Standard Prompt: “Computer keeps crashing.”
- Expert Engineered Prompt: “What are the steps to diagnose and fix a Windows 10 laptop that crashes when opening large files?”
- Job Interview Preparation
- Standard Prompt: “How to prepare for a job interview?”
- Expert Engineered Prompt: “Provide a list of common questions and effective responses for a project manager job interview in the tech industry.”
- Historical Information
- Standard Prompt: “Tell me about World War II.”
- Expert Engineered Prompt: “Summarize the key causes of World War II and its impact on European political borders.”
- Mental Health Tips
- Standard Prompt: “How to manage stress?”
- Expert Engineered Prompt: “What are five evidence-based techniques to manage work-related stress for healthcare professionals?”
- Literature Analysis
- Standard Prompt: “Analysis of Shakespeare’s works.”
- Expert Engineered Prompt: “Discuss the use of irony in Shakespeare’s ‘Macbeth’ and its effect on the development of the main theme.”
Notice the differences? Each expert-engineered prompt is designed to elicit more detailed, specific, and useful responses without additional back-and-forth or follow-ups.
Advanced Techniques in Prompt Engineering
Advanced techniques involve computational linguistics, like using text embeddings and vectors, which help models understand and generate more nuanced language:
Text Embeddings: These are mathematical representations of text in a high-dimensional space. They help models grasp semantic meanings rather than just syntactic forms.
- Example: Embeddings might cluster words like “soccer” and “football” closely together, to better connect their related meanings.
Vectors: These are used in embedding technology to represent text numerically. Vectors help the model to see similarities and differences in the meaning of words and phrases.
- Example: By comparing vectors, the model can understand that “happy” is closer in sentiment to “joyful” than to “sad.”
As AI continues to evolve, so will the need for skilled prompt engineers who can bridge the gap between human needs and machine capabilities.
For anyone looking to harness the power of AI through better communication, mastering prompt engineering isn’t just an option; it’s a necessity. Although… if you’re an AkzisAI user, you could just choose a template that already has powerful expert prompt chains, pre-engineered and ready to go, built right in.
