What Is Prompt Engineering?
Prompt engineering is the practice of designing, testing, and refining prompts to help generative AI models produce useful and relevant outputs for a particular task. If typing a prompt is like having a conversation with an AI tool, prompt engineering is about deliberately designing the instructions and context that guide that conversation.
If you’d like to explore a term or concept in more detail as you read, Quillbot’s AI Chat can provide additional explanations and examples.
Key takeaways
- Prompt engineering is the practice of designing, testing, and refining prompts to help generative AI models produce useful, relevant outputs for a specific task.
- Effective prompt engineering starts with clear instructions and relevant context. Defining the goal, specifying the desired output, and refining the prompt can help improve AI responses.
- Common prompt engineering techniques include zero-shot, few-shot, and chain-of-thought prompting. These techniques give AI models different levels of guidance for completing a task.
- Prompt engineering is used across a range of applications, from chatbots and content creation to research, software development, and specialized fields.
What is prompt engineering?
Writing a prompt is easy. In a way, it’s similar to texting a friend. You try to be as specific as possible and hope your friend, as a sensible human being, will do the same.
However, large language models (LLMs), which power many generative AI tools, are different. They can draw on vast amounts of information, but, unlike your friend, they don’t have human common sense or an inherent understanding of what they can or cannot do. This is where prompt engineering comes in: carefully designed instructions help guide how the model responds to different situations.
If a user interacted directly with a “raw” LLM without these instructions, for example, in a bank chatbot, the results could be unpredictable.
- User: “Hey, can you waive my $35 late fee?”
- AI assistant: “Sure! I want to be helpful, so I have waived that fee for you.”
This sounds plausible, but in reality the model has no access to the bank’s account system or the ability to waive a fee. It has simply generated a response that fits the conversation.
With prompt engineering:
A prompt engineer writes a “hidden script,” or the instructions that guide the LLM, before the user even types a word:
“You are a banking assistant. You cannot move money. If a user asks to waive a fee, you must strictly follow these rules: 1. Ask for their account number. 2. Check if they have been a customer for over a year. 3. If yes, trigger the [Waive_Fee] tool. 4. If no, politely decline using the bank’s official policy phrasing. Never promise a refund upfront.”
The interaction might then go like this:
User: “Hey, can you waive my $35 late fee?”
AI assistant: “I can check whether you’re eligible for a fee waiver. Please provide your account number.”
User: “[Provides account number]”
AI assistant: “You’ve been a customer for two years, so you’re eligible for a fee waiver. I’ll process your request now.”
Here, the instructions help the model respond appropriately to the situation and take into account the capabilities of the bank’s software and tools.
What is a prompt?
A prompt is the input a user gives an AI model. It can be a question, instruction, command, or piece of information that tells the model what task to perform. For example, “Summarize this article” is a prompt.
Prompt engineering builds on this idea. Rather than focusing on a single prompt, it involves designing and refining the instructions, context, and rules that help guide an AI model toward useful and consistent outputs.
For everyday users, prompting, or prompt literacy, is often a more useful way to describe the skill of communicating effectively with AI.
How does prompt engineering work?
Prompt engineering involves designing and refining prompts to help an AI model produce a useful response. The process might look like this:
| Step | What it involves | Example |
| 1. Define the goal | Tell the AI what you want it to do. | “Summarize this article.” |
| 2. Add context | Provide relevant background information. | “Explain this as if you were a science teacher speaking to a 10th-grade student.” |
| 3. Specify the output | Explain the desired format, tone, length, or other requirements. | “Give me five bullet points in a clear, neutral tone.” |
| 4. Test and refine | Review the response and adjust the prompt as needed. | “Make the explanation simpler and remove technical terms.” |
Prompt engineering is usually iterative: you give an AI a prompt, evaluate the response, identify what can be improved, and revise the prompt.
Start with: “Explain climate change.”
Then refine it: “Explain climate change to a 15-year-old in five short bullet points. Use simple language and include one everyday example.”
Common prompt engineering techniques
Different techniques can help a model handle different kinds of tasks. Here are three common examples:
| Technique | What it involves | Example |
| Zero-shot prompting | Asking the AI to perform a task without providing examples of the desired output. | “Classify this customer comment as positive, negative, or neutral: ‘The delivery was faster than expected.’” |
| Few-shot prompting | Providing a few examples of a task and its desired outputs, then asking the AI to apply the same pattern to a new input. | “‘The delivery was excellent.’ → Positive; ‘The package arrived damaged.’ → Negative. Now classify: ‘Customer service was helpful.’” |
| Chain-of-thought prompting | Guiding the model to break a complex task into intermediate reasoning steps before producing an answer. | “Solve this problem by breaking it into smaller steps before giving the final answer.” |
Prompt engineering best practices
A few simple habits can help you get better results from your prompts:
- Be specific: Tell the model exactly what you want it to do.
- Add relevant context: Give it the background information it needs to understand the task.
- Set clear requirements: If the format, length, tone, or audience matters, say so.
- Use examples when helpful: If it’s hard to describe what you want, show the model an example.
- Test and refine: If the response misses the mark, adjust your prompt and try again.
What is prompt engineering used for?
Prompt engineering is used in everything from customer service chatbots to software development. Here are a few common examples.
| Application | Example |
| Chatbots and virtual assistants | Ask for account information, follow company policies, or transfer a customer to a human agent when needed. |
| Content creation | Turn the same source material into a blog post, social media update, or student-friendly explanation. |
| Research and analysis | Compare sources, identify recurring themes, or summarize findings according to specific criteria. |
| Software development | Generate code, explain existing code, or help troubleshoot errors. |
| Specialized fields | Summarize medical records, extract information from documents, or follow industry-specific requirements. |
Prompt engineering vs context engineering
As AI systems evolve from simple chatbots to AI agents that can handle longer, multi-step tasks, prompt engineering is increasingly seen as one part of a broader practice called context engineering.
Prompt engineering focuses on the natural-language instructions that tell a model what to do and how to respond. Context engineering takes a broader view: it focuses on giving the model the right information, tools, and context at the right point in a task.
- Prompt engineering: What should the model do?
- Context engineering: What information and tools does the model need to do it?
Prompt engineering might tell it, “Recommend three family-friendly hotels and explain the pros and cons of each.”
Context engineering considers what information and tools the system makes available at that point, such as the customer’s saved preferences, previous choices, current booking information, or hotel data from a connected service.
Frequently asked questions about prompt engineering
- Is using ChatGPT prompt engineering?
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Using ChatGPT can involve prompt engineering, but everyday use of the tool is usually better described as prompting. If you’re refining a prompt to get a better answer for your own task, you’re practicing effective prompting. Prompt engineering generally refers to the more systematic design and testing of instructions for an AI application or system.
Want help writing better prompts? Try Quillbot’s AI Prompt Generator or read our guide on how to write effective ChatGPT prompts.
- What are prompt engineering techniques?
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Prompt engineering techniques are different ways of structuring prompts to guide an AI model toward a particular type of response. Common examples include zero-shot prompting, few-shot prompting, and chain-of-thought prompting.
Use Quillbot’s AI Chat if you want to go more in depth or compare these approaches.
- What is zero-shot prompting?
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Zero-shot prompting is a prompt engineering method that asks an AI model to perform a task without giving it examples of the desired output. For example, you could ask it to classify a customer comment as positive, negative, or neutral without showing it any previous examples.
You can ask Quillbot’s AI Chat for examples of zero-shot prompts for tasks such as summarizing text, generating ideas, or extracting key information.
