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What is the prompt engineering (definition & skill)
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What is prompt engineering?
Prompt engineering is the process of carefully crafting instructions (called "prompts") to guide AI language models toward generating desired outputs in a consistent, reproducible way. It involves selecting precise verbs, vocabulary, structure, and context to help AI systems understand your intent and respond accordingly.
Prompt engineers focus on understanding how AI respond by studying how different phrasings, formats, and techniques influence model behavior, and working to align AI outputs with human intent and business objectives.
In general, anyone can develop the prompt skills for example, when you are asking ChatGPT after receiving response, you rephrase the question to make the response from AI better result. This exactly prompt engineering. The main different between casual use vs profestional practice is mainly the level of systematization, testing and understanding of underlying patterns. Think of it as the bridge between human communication and machine interpretation.
Benefits of Prompt Engineering
Mastering prompt engineering delivers significant advantages that enhance both AI performance and user experience. Here are the key benefits: Improved Model Performance: Well-crafted prompts generate more accurate, relevant, and informative outputs. By providing clear instructions and proper context, you help AI models understand exactly what you need, resulting in higher-quality responses that require fewer iterations and revisions.
Reduced Bias and Harmful Content: Carefully designed prompts help mitigate bias and minimize the risk of generating inappropriate or offensive content. By controlling the input and guiding the AI's focus, you create guardrails that steer the model toward safe, ethical outputs aligned with your values and standards. Increased Control and Predictability: Prompt engineering gives you direct influence over AI behavior, ensuring consistent and predictable responses. This reliability is crucial for business applications, automated workflows, and any scenario where you need dependable outputs that align with specific requirements. Enhanced User Experience: Clear, well-structured prompts make AI interactions more intuitive and accessible. When users understand how to communicate effectively with AI models, they experience less frustration, faster results, and greater satisfaction with the technology overall. Cost and Time Efficiency: Effective prompts reduce the need for multiple attempts and manual corrections, saving both time and computational resources. Better initial outputs mean fewer API calls, lower costs, and faster project completion.
Why reproducible matters?
The core of effective prompt engineering is creating prompts that reliably produce similar quality outputs across multiple uses. This consistency play important role for a valuable, reusable tool or workflow. For example, a well-design prompt engineering can help building customer support agent more dependable and trustworthy, ensuring clients consistently receive helpful and reliable responses.
Prompt engineering techniques
Prompt engineering is constantly evolving as researchers develop new techniques and strategies. While not all methods work with every AI model, some are fundamental but others get quite advanced. Below are some fundamental approaches that every prompt engineer should know.
Direct prompts (Zero-shot)
Zero-shot prompting involves providing the model with a direct instruction or question without any additional context or examples.
An example of this is idea generation, where the model is prompted to generate creative ideas or brainstorming solutions. Another example is summarization, or translation, where the model is asked to summarize or translate some piece of content.
Few-shot prompting
Few shot prompting is one of the most effective ways to improve model performance by giving model examples of what you want it to do. The technique of adding example inputs and expected outputs to model prompt is known as "few-shot prompting". There are a few things you might think about when working with few-shot prompting:
- How are examples generated?
- How many examples selected for each prompt?
- How are examples selected at runtime? (during the application running)
- How are examples formatted in the prompt? (markdown, json etc.)
| Aspect | Approaches | Best Practices & Considerations |
|---|---|---|
| Generating Examples | You can create examples manually by hand-crafting them, use outputs from a better model, collect them from user feedback on real interactions, or automate the process with LLM self-evaluation. | Examples should be relevant, clear, and informative. Manual creation works best when core principles need deep understanding. Automated generation is better for tasks with broad, nuanced behaviors where you need many examples to cover different scenarios. |
| Example Types | Examples can be single-turn with simple input and output pairs, or multi-turn showing full conversations with corrections. | Single-turn examples are straightforward and work for most tasks. Multi-turn examples are valuable for complex tasks where you need to demonstrate common mistakes and show exactly how to correct them. |
| Number of Examples | The optimal number varies by model, task, and your specific constraints. | More examples typically improve performance, but come with tradeoffs like higher costs, increased latency, and potential confusion beyond a certain threshold. Better models need fewer examples and hit diminishing returns faster. Always experiment to find your optimal number. |
| Selecting Examples | Examples can be selected randomly, by semantic similarity to the input, by keyword matching, or based on constraints like token size. | Semantic similarity generally produces the best results, but the importance varies by model and task. Don't assume one method is always best, test different selection strategies for your specific use case. |
| Formatting in Prompts | Examples can be embedded as strings in the system prompt or formatted as separate message objects in the conversation. | For string format, use clear syntax like ChatML, XML, or TypeScript and make input/output boundaries obvious. For message format, assign distinct names like "example_user" and "example_assistant" to differentiate from the actual conversation. |
| Tool Call Examples | Formatting tool call examples requires following model-specific requirements for message sequences. | Some models require ToolMessages immediately after tool calls, while others need AIMessages after ToolMessages. Check your specific model's requirements as you may need dummy messages to satisfy API constraints. Test both string and message formats to see which performs better. |
Chain-of-thought (CoT) prompting
In 2022, Google introduced Chain-of-Thought prompting in an paper named Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.
Chain-of-thought prompting improves language model performance by explicitly asking the model to generate a step-by-step explanation before arriving at a final answer. Instead of jumping to a conclusion, the model methodically works through its logic, breaking complex problems into manageable steps. This prevents reasoning failures that occur when models skip crucial steps or make unsupported logical leaps.
CoT is effective because it focuses the model's attention mechanism. By decomposing problems into sequential reasoning steps, the model concentrates on one aspect at a time, minimizing errors that arise from processing too much information simultaneously.
const chainOfThoughtPrompt = ` You are an expert AWS Solutions Architect. When answering, first think step by step about the trade-offs and reasoning (this reasoning is hidden from the user). Then provide only the final concise response to the user.
Customer Question: ${customerQuestion}
Step-by-step reasoning (hidden from user):
1. Analyze the AWS service(s) involved.
2. Consider trade-offs such as cost, performance, availability, and scalability.
3. Compare alternatives if relevant.
4. Conclude with the best recommendation logic.
Final Answer (to user): Give a concise, clear, practical explanation under 100 words. `
Use cases and examples of prompt engineering
Prompt engineering has practical applications across industries. Here are the most common use cases showing how professionals leverage this skill to achieve better AI outputs.
Content Creation and Writing
Creative Writing: Guide AI to generate stories by specifying genre, tone, and plot elements. Example: "Write a short story about a young woman who discovers a magical portal in her attic." Summarization: Extract key information from long documents efficiently. Example: "Summarize the main points of the following news article on climate change." Translation: Ensure accurate translations by specifying source and target languages. Example: "Translate the following text from English to Spanish: 'The quick brown fox jumps over the lazy dog.'"
Question Answering Systems
Open-Ended Questions: Get comprehensive explanations with prompts like "Explain the concept of quantum computing and its potential impact on the future of technology." Specific Questions: Retrieve precise information with targeted prompts such as "What is the capital of France?" or "According to the provided text, what are the main causes of deforestation?" Hypothetical Scenarios: Explore possibilities with prompts like "What would happen if humans could travel at the speed of light?"
Software Development
Code Generation: Accelerate development with prompts like "Write a Python function to calculate the factorial of a given number." Code Debugging: Identify and fix errors using prompts such as "Debug the following Java code and explain why it is throwing a NullPointerException." Code Optimization: Improve performance by requesting "Optimize the following Python code to reduce its execution time."
AI Image Generation
Photorealistic Images: Create detailed visuals with descriptive prompts: "A photorealistic image of a sunset over the ocean with palm trees silhouetted against the sky." Artistic Styles: Generate art in specific styles: "An impressionist painting of a bustling city street with people walking under umbrellas in the rain." Image Editing: Modify existing images with precise instructions: "Change the background of this photo to a starry night sky and add a full moon."