After refactoring

import hashlib

import sqlite3

from typing import Optional

class DatabaseHelper:

"""Utility class for database interactions."""

def __init__(self, db_path: str):

    self.db_path = db_path

def execute_query(self, query: str, params: tuple = ()) -> Optional[list]:

    """Execute query and return fetched results if available."""

    with sqlite3.connect(self.db_path) as conn:

        cur = conn.cursor()

        cur.execute(query, params)

        if query.strip().lower().startswith("select"):

            return cur.fetchall()

        conn.commit()

    return None

class AuthManager:

"""Handles user registration and login authentication."""

def __init__(self, db_helper: DatabaseHelper):

    self.db = db_helper

def _hash_password(self, password: str) -> str:

    """Hash the password using SHA-256."""

    return hashlib.sha256(password.encode()).hexdigest()

def register_user(self, username: str, password: str):

    """Register new users by saving hashed passwords."""

    hashed = self._hash_password(password)

    self.db.execute_query(

        "INSERT INTO users (username, password) VALUES (?, ?)",

        (username, hashed)

    )

def login(self, username: str, password: str) -> bool:

    """Validate user credentials against stored hashes."""

    result = self.db.execute_query(

        "SELECT password FROM users WHERE username=?", (username,)

    )

    if not result:

        return False

    stored_hash = result[0][0]

    return stored_hash == self._hash_password(password)

This refactor introduces a single-responsibility design while retaining identical functionality. Claude also adds docstrings and improves readability — demonstrating incremental improvement without breaking compatibility.

You could then extend this prompt with:

“Claude, generate unit tests for bothDatabaseHelper andAuthManager to verify behavior after refactoring.”

Claude would return a full, runnable test suite usingpytest that validates the refactor before committing.

Clarification Table: Refactoring Workflow Summary

Incremental refactoring with Claude Code provides a safe, structured path to modernizing large projects. Instead of high-risk rewrites, developers can evolve their systems through steady, validated improvements. The inclusion of self-documenting features — like docstrings and changelogs — ensures long-term maintainability and clarity.

By embracing small, Claude-assisted iterations, teams gain confidence that every change strengthens their codebase without disrupting stability. In the next section, we’ll build upon this approach to explore multi-file consistency and documentation alignment, ensuring that as your code evolves, your architecture and written documentation evolve right alongside it.

10.4 Maintaining Consistency Across Multiple Prompts

When working with Claude Code on large projects, developers often break their interactions into multiple prompts — one for each module, feature, or debugging session. However, as projects grow, maintaining consistency across these prompts becomes a key challenge. Without continuity, Claude might make style changes that drift from the team’s standards, rename variables differently, or interpret prior context inconsistently.

Claude’s conversational intelligence can maintain context within a session, but developers must still manage prompt consistency manually when working across multiple sessions or team members. In this section, we’ll explore how to maintain a coherent tone, structure, and coding standard across all interactions with Claude. You’ll learn techniques to establish persistent context, enforce style rules, and keep Claude “on the same page” as your team throughout long-running projects.

Concept Development

Claude doesn’t store long-term memory between separate prompts; it only uses the context you provide. That means maintaining consistency requires deliberate prompt engineering strategies and a disciplined workflow. Here are three foundational principles for achieving that:

1. Persistent Context Sharing — Keep a lightweight “Claude Context File” (e.g., claude_context.md) that summarizes coding conventions, architecture, and project goals. You can paste this at the start of each new Claude session to reestablish alignment.
2. Style and Tone Anchoring — Define specific conventions once and reuse them consistently. This includes naming standards, docstring formats, test patterns, and file structures. Claude can follow these rules exactly if they’re restated each time.
3. Incremental Session Linking — When a task spans multiple prompts, remind Claude of what was previously done. For example: “Here’s the class you helped refactor earlier — now let’s add unit tests to it.” This prevents Claude from drifting in its assumptions about code intent or dependencies.

Together, these techniques ensure that each interaction — no matter how isolated — contributes to a unified, predictable project output.

Hands-On Example: Using a Shared Context File

Imagine you’re developing a FastAPI-based internal service with multiple engineers using Claude for different modules. Without a shared context, each developer’s prompts might result in inconsistent structure. To fix this, you create a central context file named claude_context.md that defines the project’s expectations.