In today’s rapidly developing AI technology, prompt engineering is becoming a core skill that product managers and technical practitioners must master. This article systematically sorts out the basic concepts, design processes, common frameworks, and practical techniques of prompts to help you move from “knowing how to use AI” to “using AI well”.
I hope anyone who finishes this series of tutorials can master how to write production-grade prompts and never have to look for fragmentary knowledge points again!
Understanding Prompts: The AI’s Instruction Language
What is a prompt word?
In the context of artificial intelligence, a prompt is the input text provided by a user to a model to guide it in generating a specific response. It can be a simple question, a string of keywords, or a detailed instruction with complex instructions, contextual information, or even code snippets
Essentially, prompts are the “natural language” through which we communicate with generative AI models and assign tasks.
A well-designed prompt can typically contain the following four core elements:
- Instruction: Clearly informs the model of the specific tasks it needs to perform. For example, “Summarize the following article”, “Classify this text as positive, neutral, or negative”, “Generate a marketing email”.
- Context: Provides context or external knowledge to help the model better understand the task context, leading to more relevant responses. For example, when asking a model to write a report, provide context about the target audience (“The readers of this report are investors without any technical background”).
- InputData: The specific content that the model needs to deal with. For example, the full text of an article that needs to be summarized, or user reviews that need to be categorized.
- OutputIndicator: Specifies the type, format, or style of the model’s output. For example, “Please return results in JSON format”, “Answers should use bulleted lists”, “Tone should be professional and rigorous”.
Not all prompts must contain all four elements, and their specific composition depends on the complexity and needs of the task (5). However, understanding these components is the first step in systematically designing efficient prompts.
What does a product manager need to do?
In the process of a product from scratch, it is not easy to do a good job in the role of product manager, in addition to the well-known writing requirements, writing requirements, writing requirements, there are many things to do. The product manager is not what you think, but will only ask you for trouble, make a request:
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Why is it important to design high-quality prompts?
The quality of the prompt directly determines the quality, relevance, and accuracy of the AI’s output content. This is especially critical in practical application scenarios.
A vague or poorly structured prompt can lead to inconsistent, off-topic, or even factual errors (i.e., “hallucinations”), which is unacceptable for products that rely on AI for stable services.
Instead, a well-crafted prompt provides an AI assistant with a clear job statement, effectively guiding the model to produce high-quality, reliable results that align with expectations.
This not only improves the user experience but also significantly reduces the business risks and manual correction costs caused by AI output errors. Therefore, mastering the skill of prompt writing means mastering the key to precise control and utilization of AI capabilities.
Two types of prompts
In practice, we can divide prompts into two main categories: daily conversation prompts and production-grade prompts.
Understanding the difference between the two is a critical step for product and business personnel to transform from ordinary users of AI to professional AI application designers.
1) Daily conversation prompts
These prompts are unstructured, imprompt natural language queries that we use daily when interacting with chatbots like ChatGPT. For example, “Recommend me a few sci-fi movies” or “Explain what a black hole is.” These prompts are characterized by their simplicity, directness, and rely on the model’s vast pre-trained knowledge base for answers. They are ideal for exploratory, informal knowledge acquisition and entertainment scenarios.
2) Production-level prompts
Production-grade prompts are specifically designed for integration into software applications or business processes, with the primary goal of ensuring outputReliability, consistency, and scalability。
The fundamental difference from everyday conversation prompts is that production-grade prompts are “engineered” instructions that must consistently produce predictable, format-compliant outputs under a wide range of input conditions.
A core cognitive shift lies in:
Everyday conversation is a kind of “conversation”, while production-grade applications are one“Instructions”.
Professional applications cannot afford AI’s “mood-based” answers, and it requires that every service meet an expected standard.
Production-grade prompts greatly reduce the ambiguity of model interpretation by providing clear roles, strict rules, clear context, and fixed output formats, thereby constraining the “creativity” of AI within a controllable business framework, making it a stable and reliable automation tool.
Production-level prompts contain a closed loop from requirements definition to solution. Therefore, for product managers, learning to design production-level prompts is essentially learning how to accurately translate business requirements into specifications that AI can stably execute.
What is prompt word engineering?
1) Definition and process
Prompt engineering is an emerging discipline that focuses on developing and optimizing prompts to help users use language models more effectively to complete various complex tasks.
The process of writing production-level prompts is the practice of prompt engineering. This process does not happen overnight, but is an iterative cycle similar to software development or machine learning.
It usually includes requirements analysis, initial design, test evaluation and continuous optimization. Prompt engineers need to bridge the gap between end-user needs and the capabilities of large language models, and through continuous experimentation and adjustment, they need to find input text combinations that can stimulate the best performance of the model.
2) The value of prompt word engineering
For enterprises, prompt engineering offers a more efficient and flexible way to leverage AI capabilities than fine-tuning.
Cost and efficiency: Model fine-tuning requires retraining of the parameters of the model itself, which not only requires massive annotation data, but also has high computational costs and long cycles. Prompt engineering stimulates the existing knowledge and ability of the model by optimizing the input instructions without changing the model’s own parameters. This enables businesses to prototype and iterate on AI applications at a lower cost and at a faster pace.
Flexibility and control: Prompts can be seen as the “soft logic” of the application, which can be modified and deployed at any time without retraining the entire model. This flexibility allows product teams to respond quickly to business changes and user feedback, continuously optimizing AI capabilities.
Mastering prompt engineering means that product and business teams have gained the ability to directly shape and control AI behavior, enabling them to translate AI technology into tangible business value faster.
How to design production-grade prompts
Designing production-grade prompts is an art that combines logic and creativity. It requires us to systematically build a communication bridge with AI like a product manager planning functions and an engineer designing an architecture. This section will delve into the design process, common frameworks, and core techniques for prompts, providing a reusable methodology for product and business personnel.
Prompt design process: from requirement to implementation
A successful prompt needs to follow a rigorous design process. We can compare it to a miniature product development cycle to ensure that the final output meets business needs precisely.
Step 1: Needs Analysis (“Why”)
Before you start writing, you must first clearly define your business goals and user needs. Ask yourself a few key questions:
- What problem do we solve? (e.g., reduce customer service response time, increase click-through rate of marketing copy)
- What role does AI play in this process? (e.g., information extractors, content generators, decision analysis)
- What does a “successful” output look like? (Define the criteria for successful output, e.g., an abstract of no more than 300 words with three key points; a properly formatted JSON object)
- Who are the target users? (e.g., agents who need quick access to information, marketing specialists looking for ideas)
This step is equivalent to writing a product requirements document (PRD) or user story and is the cornerstone of the entire design process.
Step 2: Initial prompt design (“What”)
Based on the requirements analysis, draft the first version of the prompt. At this point, the core elements mentioned earlier should be consciously included:
- Clear instructions: Clarify the task.
- Necessary context: Give AI characters or provide business context.
- Clear output format: Tell the AI what your desired outcome will look like.
Step 3: Test and Evaluate (“How Well”)
Test prompts with a range of input data that should cover typical use cases and potential edge cases. Then, evaluate the AI’s output based on the success criteria defined in the first step. The dimensions of the assessment will be described in detail in subsequent notes
Step 4: Improve Loop
Based on the test results, the prompt words are continuously optimized and refined. This can include adjusting wording, adding or modifying examples, reinforcing constraints, changing assigned roles, or even switching models. It’s a continuous looping process until the prompt consistently produces high-quality output under various tests.
Commonly used prompt writing frameworks
Prompt frameworks are like templates for writing, they provide a proven structure that helps us design prompts without missing key information, thereby systematically improving the quality and stability of prompts.
To help product and business personnel quickly choose the right tool, the following table compares several mainstream prompt frameworks and their applicable scenarios.
TAG framework: simple and efficient
The TAG framework is one of the simplest and most straightforward structures, ideal for quick task definition on a daily basis.
- T (Task): What do you want AI to do? (e.g., “Write a blog post about AI”)
- A (Action): How should AI be performed? (e.g., “Use a humorous and engaging style”)
- G (Goal): What is the ultimate goal you want to achieve? (e.g., “Attracting beginners interested in AI”)
APE Framework: Emphasize intent
The APE framework is similar to TAG but focuses more on getting the model to understand the “purpose” behind the task, which helps the model make smarter decisions
- A (Action): The specific action that the AI needs to perform. (e.g., “Analyze this customer feedback report”)
- P (Purpose): Why do you do this? (For example: “To find out the three main reasons for customer churn”)
- E (Expectation): The desired output format or outcome. (For example: “Presented as a bulleted list and sorted by importance”)
COAST Framework: Addressing complex strategies
When faced with complex or strategic tasks that require the integration of multiple factors, the COAST framework provides a comprehensive structure for thinking.
- C(Context-Background): Provide the necessary background information. (e.g., “Our company’s sales fell by 15% in the third quarter”)
- O (Objective): Set a high-level, measurable goal. (e.g., “20% increase in sales in Q4”)
- A (Actions): The steps that the model needs to consider or perform. (For example: “Analyze market trends, come up with three marketing strategies, evaluate the budget for each”)
- S (Scenario): Describing a specific environment or situation. (For example: “We are facing pressure from two of our main competitors to cut prices”)
- T (Task): A specific instruction given to the AI. (For example: “Based on all of the above information, develop a detailed draft marketing plan for the fourth quarter”)
LangGPT framework: Build exclusive AI agents
LangGPT is a highly structured prompt design paradigm that compares prompt design to object-oriented programming, aiming to create reusable AI agents with specific roles, rules, and workflows. It usually uses the Markdown format and contains the following modules
- Role: Define the core role of the AI
- Profile: Describe the character’s background, expertise, and language style
- Rules: The rules and constraints that the AI must adhere to
- Workflow: The steps by which the AI interacts with a user or completes a task
- Initialization: AI’s opening and leading
For example, the LangGPT prompt for a “Tang Dynasty poet” will define in detail their identity as a poet, the poetic genre they are good at (such as seven-character rhythm poems), the metrical rules they must follow, and how they interact with the user (asking the user to provide a theme and form). This framework is ideal for building virtual assistants or expert systems that require long-term consistency and expertise.
Core prompt tips (important)
After mastering the framework, a series of specific techniques are required to further polish and optimize prompts to meet different task needs.
Zero-shot vs. Few-shot prompts
Zero-shot prompt: Directly ask the model to complete the task without providing any examples. This depends entirely on the general capabilities that the model learns during the pre-training phase.
apply: Suitable for scenarios where tasks are simple and straightforward (e.g., “translate ‘hello’ to English”) or quickly verify a concept of an AI function.
Small sample cue: Provide one or more (usually no more than 5) complete task examples (“shots”) in the prompt to show the model the desired input and output formats. This is a powerful “In-context Learning” technique.
apply: Extremely effective when you need to force the model to follow a specific output format, tone, or style. For example, when generating customer service emails, providing 2-3 email examples that meet the company’s specifications can greatly improve the quality of newly generated emails.
Providing examples is one of the most direct and effective ways to guide LLMs as a pattern matching engine. It transforms vague “instructions” into clear “patterns”, greatly reducing the probability of the model “guessing wrong” user intent, and is the most powerful tool for product managers to improve AI performance without code-level development.
Decomposition
Decomposition, also known as “prompt chaining,” is the process of splitting a large complex task into a series of simpler, smaller subtasks that are then completed sequentially with multiple prompts.
task: Analyze a lengthy annual financial report.
Prompt 1 (Extract)“From the following earnings report, extract all key financial data (revenue, profit, cash flow) and output it in JSON format. ”
Prompt 2 (Analysis)“Based on the following financial data, three main financial risks are identified. ”
Prompt 3 (generated)“Based on the identified financial risks, draft an early warning email to management. ”
This approach reduces the complexity of a single task, allowing the model to complete each step more focused and accurately.
Role Prompting
That is, giving the AI a specific identity or expert role, such as “You are a marketing director with 20 years of experience” or “Let’s say you are a rigorous legal advisor.”
Principle of action: Character prompts provide the model with a powerful context that activates the parts of its knowledge base that are most relevant to that persona, resulting in a more aligned tone, style, and expertise in its output.
skill: The more specific the definition of the role, the better. “You’re a data scientist” is a good start, but “You’re a data scientist focusing on customer churn warning models in the B2B SaaS space” can bring more accurate output.
Chain of Thought (CoT)
This is a technique in which the guided model demonstrates its “thought process” before giving the final answer. By incorporating “Let’s think step by step” in prompts or providing examples that include reasoning steps, the model’s accuracy in logical reasoning, arithmetic, and complex problem-solving can be significantly improved.
Business applications: Solve a multi-step calculation problem, such as “A package is 50 yuan per month, including 1,000 minutes of calls, and the excess is 0.1 dollars per minute.” Plan B is 70 yuan per month, unlimited calls. If the customer talks for 1200 minutes last month, which package is more cost-effective? With CoT, the model will first calculate the total cost of package A, then compare it with package B, and finally give a conclusion, the process is clear and error-free.
Zero-shot CoT: A very practical trick, just add a sentence after your question”Let’s think about it step by step(Let’s think step-by-step) can effectively stimulate the reasoning ability of the model without providing examples.
It should be noted that as reasoning models become more popular, the need for this hint technique may be declining
Tree of Thoughts (ToT)
ToT is an advanced version of CoT. When there are multiple possible solutions to a problem, ToT allows the model to explore multiple “branches of thinking” at the same time, evaluate each branch, and even “backtrack” when it finds that one branch is a dead end, and then explore other paths.
apply: Conducting strategic planning or brainstorming. For example: “Develop a go-to-market strategy for our new product. Please explore from the three directions of ‘online marketing’, ‘offline activities’ and ‘channel cooperation’, and evaluate the advantages and disadvantages and potential risks of each direction respectively. The model will think deeply about these three directions as different branches.
Self-Criticism / Reflexion
This is a technique that allows the model to evaluate and improve its own output. After the model generates a preliminary answer, you can add a prompt, such as: “Please check your answer.” Is there a factual error? Is the logic rigorous? What can be improved? This leverages the model’s powerful text analysis capabilities to optimize its own generative capabilities.
Negative constraints
Clearly inform the modelNoDo what. For example, “Do not include any technical jargon in your responses” or “Do not use exaggerated words such as ‘revolutionary’ or ‘disruptive’ in the generated copy.” When used correctly, this technique can be very effective in “shaping” the output content, ensuring that it meets specific communication norms.
Some tips that need to be analyzed according to the usage scenario:
Negative constraints: The specific effect is not necessarily good, nor is it necessarily bad. Too many or complex negative constraints can confuse the model, leading to reduced performance. It is recommended to keep negative constraints concise and clear.
Role tips: In needGenerate creative contentorImitate a particular style(such as writing marketing copy, playing customer service), the role prompt effect is better. But in pure demandFactual accuracy(e.g., extracting data from text), its role is relatively limited, and clear instructions and output format definitions are more important.
Key parameters: fine-tune the AI’s style
In addition to optimizing the prompt text, adjusting the model’s generation parameters is also an important part of prompt word engineering. For product and business personnel, these parameters can be understood as knobs that control the style of AI output.
Temperature
definition:Temperature controls how random the model is when generating the next word.
A lower temperature value (e.g., 0.2) makes the model more inclined to choose the word with the highest probability, resulting in more predictable and conservative text. A higher temperature value (e.g., 0.8 or higher) “flattens” the probability difference between words, giving the less probable words a chance to be selected, resulting in more varied, creative, and even unexpected text.
Application in prompt design：
- Creative tasks: When your goal is to brainstorm, write marketing copy, or create a story, turn up the temperature (e.g., 0.8-1.0) to stimulate the creativity of the model.
- Factual tasks: When your goal is to get accurate facts, summarize text, or generate code, turn the temperature down (e.g. 0.2-0.5) to reduce factual errors caused by model “free play”.
Top-p (Nuclear Sampling)
definition:Top-p, also known as nucleus sampling, is a method of dynamically controlling the range of vocabulary selection. It sets a probability threshold p (e.g. 0.9), and the model starts with the words with the highest probability output, adds up their probabilities until the sum reaches p, and then the model samples only from this “core” vocabulary.
Unlike the fixed number of Top-k, the vocabulary size of Top-p changes dynamically, and the more certain the model, the smaller the vocabulary set. The more uncertain the model, the larger the vocabulary set.
Application in prompt design：
- Balanced generation:Top-p is often considered a better control method than Temperature, as it avoids the selection of outrageous words with a very low probability and dynamically adjusts creativity based on the certainty of the context. A common value is between 0.9 and 0.95, which provides good diversity while maintaining coherence.
- Fine tuning: For conversational robots or Q&A systems that require a high degree of coherence, the top-p (e.g., 0.8) can be appropriately lowered to make its answers more focused.
Top-k
definition:Top-k is the simplest method of sampling control. It directly limits the model to only choose from the highest probability k words when generating the next word. For example, if you set the Top-k to 10, the model will only choose from 10 alternatives no matter what the situation is.
Application in prompt design：
- Focus on content generation: When you need to ensure that the output is strictly limited to a topic, you can use a smaller k value (e.g. 5-20).
- Comparison with Top-p: Imagine ordering, Top-k is like a fixed set menu, always only k dishes to choose from. Top-p, on the other hand, is like a buffet, offering dishes that account for 90% of the total popularity, and the number of dishes will change according to everyone’s taste preferences that day. As a result, Top-p is generally smarter and more flexible.
In practical applications, it is common to adjust one of Temperature and Top-p instead of both to avoid unpredictable effects.
For product and business personnel, understanding the meaning of these parameters means that you can change the output style of AI applications to suit different business scenarios by adjusting these parameters without changing the prompt text.
