最近，X 和 Reddit 上的用户报告说，使用 Claude 3.5 Sonnet 编写代码让他们感觉“非常强大”。其中一条特别引起关注的帖子是 YCombinator 首席执行官 Garry Tan 的一条推文。  Garry 分享了他们的一位 YCombinator Reddit 社区成员的以下帖子，介绍了如何使用 Claude 3.5 Sonnet 在实现流行功能的同时将他们的生产力提高了 10 倍。 该帖子还强调了做出合理的架构和基础设施决策作为日常 LLM 使用的关键部分的重要性。   https://twitter.com/garrytan/status/1814732623073456165?embedable=true 尽管 Claude 3.5 等 LLM 具有突出的优势，但它们在保持记忆和上下文方面仍然存在局限性。可以使用几种开发策略来解决这些限制。这些策略围绕软件开发基础知识展开，所有开发人员都通过经验磨练这些基础知识，但在用通俗易懂的英语提示 LLM 时往往很容易忽略这些基础知识。 将开发人员日常使用的相同基本原则（Reddit 作者称之为架构决策）应用于 LLM 交互，可以产生高度模块化、可扩展且有据可查的代码。 以下是一些可应用于 LLM 辅助软件开发的关键编码原则和开发实践，以及 Python 中的实际示例： 示例中的所有提示均与 Claude Sonnet 3.5 一起使用。 基于组件的架构 为了更容易向 LLM 描述每个逻辑组件并获得准确的组件，将代码库分解为小的、定义明确的组件。   # database.py class Database: def __init__(self, sql_connection_string): .... def query(self, sql): .... # user_service.py class UserService: def __init__(self, database): self.db = database def get_user(self, user_id): return self.db.query(f"SELECT * FROM users WHERE id = {user_id}") # main.py db = Database("sql_connection_string") user_service = UserService(db) user = user_service.get_user(123) 抽象层 为了隐藏复杂的实现，请使用抽象层，并使用沿途记录的每个组件的详细信息来关注更高级别的抽象。   # top-level abstraction class BankingSystem: def __init__(self): self._account_manager = AccountManager() self._transaction_processor = TransactionProcessor() def create_account(self, acct_number: str, owner: str) -> None: self._account_manager.create_account(acct_number, owner) def process_transaction(self, acct_number: str, transaction_type: str, amount: float) -> None: account = self._account_manager.get_account(acct_number) self._transaction_processor.process(account, transaction_type, amount) # mid-level abstractions class AccountManager: def __init__(self): def create_account(self, acct_number: str, owner: str) -> None: def get_account(self, acct_number: str) -> 'Account': class TransactionProcessor: def process(self, account: 'Account', transaction_type: str, amount: float) -> None: # lower-level abstractions class Account(ABC): .... class Transaction(ABC): .... # concrete implementations class SavingsAccount(Account): .... class CheckingAccount(Account): .... class DepositTransaction(Transaction): .... class WithdrawalTransaction(Transaction): .... # lowest-level abstraction class TransactionLog: .... # usage focuses on the high-level abstraction cart = ShoppingCart() cart.add_item(Item("Book", 15)) cart.add_item(Item("Pen", 2)) total = cart.get_total() 清晰的接口定义 为了在寻求 LLM 帮助时使任务更易于管理，请为每个组件定义清晰的接口，并专注于分别实现它们。   class PaymentProcessor(ABC): @abstractmethod def process_payment(self, amount: float, card_no: str) -> bool: .... class StripeProcessor(PaymentProcessor): # stripe specific implementation def process_payment(self, amount: float, card_no: str) -> bool: .... class PayPalProcessor(PaymentProcessor): # paypal specific implementation def process_payment(self, amount: float, card_no: str) -> bool: .... 单一职责原则 为了防止出现幻觉并限制范围，请一次专注于一小部分，并确保每个类/函数都有单一、明确定义的职责。逐步开发以更好地控制生成的代码。   class UserManager: # user creation logic def create_user(self, username, email): ... class EmailService: # send welcome email logic def send_welcome_email(self, email): .... class NotificationService: # send sms notification def send_sms(self, username, email): ... # Usage user_manager = UserManager() email_svc = EmailService() user = user_manager.create_user("hacker", "hacker@hackernoon.com") email_svc.send_welcome_email("hacker_writer@gmail.com") 一致的命名约定 为了简化向 LLM 描述代码结构并理解他们的建议，请使用清晰且一致的命名规则。   # classes: PascalCase class UserAccount: pass # functions and variables: snake_case def calculate_total_price(item_price, quantity): total_cost = item_price * quantity return total_cost # constants: UPPERCASE_WITH_UNDERSCORES MAX_LOGIN_ATTEMPTS = 3 # private methods/variables: prefix with underscore class DatabaseConnection: def __init__(self): self._connection = None def _connect_to_database(self): pass 代码模板 根据需求生成具体的实现，为常见的代码结构创建骨架代码，并将其用作启动代码。   # Todo item - pydantic Model class TodoItem(BaseModel): id: Optional[int] = None title: str description: Optional[str] = None completed: bool = False todo_db = [<insert mock data>] @app.get("/todos", response_model=List[TodoItem]) async def get_todos(): # TODO: retrieve all todos @app.get("/todos/{todo_id}", response_model=TodoItem) async def get_todo(todo_id: int): # TODO: retrieve specific todo @app.post("/todos", response_model=TodoItem) async def create_todo(todo: TodoItem): # TODO: create todo @app.put("/todos/{todo_id}", response_model=TodoItem) async def update_todo(todo_id: int, todo: TodoItem): # TODO: update todo @app.delete("/todos/{todo_id}") async def delete_todo(todo_id: int): # TODO: delete todo 文档驱动开发 为了帮助指导 LLM，请在实施之前为每个组件编写简明的文档。此外，使用 LLM 本身生成可以指导未来提示的文档字符串。 迅速的：   Use a Documentation Driven Development approach to write clear, concise docstrings for the following python function calculate_discount(price, discount_percentage) takes in - price (float), original price of the item. - discount_percentage (float), discount percentage range(0-100). returns - price after discount (float) raises - ValueError, when discount_percentage is out of range example - calculate_discount(100, 10) -> 10.0 回复：   def calculate_discount(price: float, discount_percent: float) -> float: """ Calculate the discounted price of an item. Args: price (float): original price for the item. discount_percentage (float): discount percentage range(0-100). Returns: float: The price after applying the discount. Raises: ValueError: If discount_percentage is not between 0 and 100. Example: >>> calculate_discount(100, 20) 80.0 """ if not 0 <= discount_percent <= 100: raise ValueError("Discount percent must be between 0 and 100") discount_amount = price * (discount_percent / 100) return price - discount_amount 测试驱动开发 为了帮助 LLM 实现并特别处理极端情况，请在实现实际功能之前自行编写测试或使用 LLM 进行测试。LLM 不会在内部运行代码，但事先进行测试以确保生成的代码满足功能要求可能有助于捕获 LLM 生成的代码中的错误。 测试用例：   def is_palindrome(s: str) -> bool: """check if string is palindrome.""" return s == s[::-1] class TestPalindrome(unittest.TestCase): def test_palindrome(self): self.assertTrue(is_palindrome("racecar")) self.assertTrue(is_palindrome("A man a plan a canal Panama")) self.assertFalse(is_palindrome("hello")) self.assertTrue(is_palindrome("")) # Edge case: empty string 迅速的：   given the test case <insert test>, use Document Driven Development to write a function for TestPalindrome 回复：   def is_palindrome(s: str) -> bool: """ Check if a string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward, ignoring spaces, punctuation, and capitalization. Args: s (str): The input string to check. Returns: bool: True if the input is a palindrome, False otherwise. Examples: >>> is_palindrome("racecar") True >>> is_palindrome("A man a plan a canal Panama") True >>> is_palindrome("hello") False >>> is_palindrome("") True """ # Remove non-alphanumeric characters and convert to lowercase cleaned = ''.join(char.lower() for char in s if char.isalnum()) # Compare the cleaned string with its reverse return cleaned == cleaned[::-1] 设计模式库 与LLM讨论多种实现细节，维护一个常用设计模式库以供参考。   # Singleton pattern class DatabaseConnection: _instance = None def __new__(cls): if cls._instance is None: cls._instance = super().__new__(cls) # initialize database connection return cls._instance # Factory pattern class AnimalFactory: @staticmethod def create_animal(animal_type): if animal_type == "dog": return Dog() elif animal_type == "cat": return Cat() else: raise ValueError("Unknown animal type") # Observer pattern class Subject: def __init__(self): self._observers = [] def attach(self, observer): self._observers.append(observer) def detach(self, observer): self._observers.remove(observer) def notify(self): for observer in self._observers: observer.update() # Adapter pattern class LLMAdapter: def __init__(self, llm_service): self.llm_service = llm_service def generate_code(self, prompt): llm_response = self.llm_service.complete(prompt) return self.extract_code(llm_response) def extract_code(self, response): pass 代码审查清单 为了确保质量和一致性，请创建一个用于审查 LLM 生成的代码的清单。   # Code Review Checklist ## Functionality - [ ] Code performs the intended task correctly - [ ] Edge cases are handled appropriately ## Code Quality - [ ] Code follows project's style guide - [ ] Variable and function names are descriptive and consistent - [ ] No unnecessary comments or dead code ## Performance - [ ] Code is optimized for efficiency - [ ] No potential performance bottlenecks ## Security - [ ] Input validation is implemented - [ ] Sensitive data is handled securely ## Testing - [ ] Unit tests are included and pass - [ ] Edge cases are covered in tests ## Documentation - [ ] Functions and classes are properly documented - [ ] Complex logic is explained in comments 模块化提示 LLM 在具有明确结构的情况下工作效果最佳，因此请制定策略将编码任务分解为更小的提示。遵循有组织的方法有助于生成工作代码，而无需提示 LLM 多次重新更正生成的代码。 迅速的：   I need to implement a function to calculate the Fibonacci number sequence using a Document Driven Development approach. 1. Purpose: function that generates the Fibonacci sequence up to a given number of terms. 2. Interface: def fibonacci_seq(n: int) -> List[int]: """ generate Fibonacci sequence up to n terms. Args: n (int): number of terms in the sequence Returns: List[int]: fibonacci sequence """ 3. Key Functionalities: - handle input validation (n should always be a positive integer) - generate the sequence starting with 0 and 1 - each subsequent number is the sum of two preceding ones - return the sequence as a list 4. Implementation Details: - use a loop to generate the sequence - store the sequence in a list - optimize for memory by only keeping the last two numbers in memory if needed 5. Test Cases: - fibonacci_seq(0) should return [] - fibonacci_seq(1) should return [0] - fibonacci_seq(5) should return [0, 1, 1, 2, 3] 虽然上述所有示例似乎都很简单，但遵循模块化架构和有效快速工程等基础实践，并在 LLM 辅助开发中采用稳固的结构化方法，在规模上会产生很大的不同。通过实施这些实践，更多的开发人员可以最大限度地利用 LLM 的优势，从而提高生产力和代码质量。  LLM 是功能强大的工具，在容易被忽视的软件工程原则的指导下，效果最佳。内化这些原则可能是优雅编写的代码和随机生成的 之间的区别。 “大泥球” 本文的目的是鼓励开发人员在使用 LLM 时始终牢记这些做法，以便将来编写高质量的代码并节省时间。随着 LLM 的不断改进，基础知识将变得更加重要，以便最大限度地发挥其作用。 要深入了解软件开发原则，请查阅这本经典教科书：  Robert C. Martin 撰写的《清洁架构：软件结构和设计工匠指南》。 如果您喜欢这篇文章，请继续关注下一篇文章，我们将深入探讨 LLM 辅助开发的详细工作流程。请在评论中分享您认为重要的任何其他概念！谢谢。

Walkthroughs, tutorials, guides, and tips. This story will teach you how to do something new or how to do something better.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

Read My Stories

該音頻是用故事的原始語言製作的！

如何利用 LLM 实现有效且可扩展的软件开发

About Author

註釋

標籤

这篇文章刊登在

Related Stories

比特币 UTXO 模型，为独特的生态系统提供动力

Telegram：加密岛通往大陆的桥梁

数字游民请注意：您需要了解泰国新推出的 DTV 签证

看不见的层面：为什么用户访谈是不可替代的资产

比特币 UTXO 模型，为独特的生态系统提供动力

Telegram：加密岛通往大陆的桥梁

数字游民请注意：您需要了解泰国新推出的 DTV 签证

看不见的层面：为什么用户访谈是不可替代的资产

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps