Every developer working with AI assistants has experienced this frustration: asking for the same fix multiple times, watching AI generate outdated patterns, or spending hours debugging code that looked perfect but failed in production.
The issue isn’t with AI capabilities - it’s with how we’re using these tools. Most developers jump straight into code generation without giving AI the context it needs to make informed decisions.
Common AI Coding Problems
The Pattern Repetition Problem
AI assistants often suggest the same flawed approaches because they lack project-specific context. Without understanding your architecture, dependencies, or constraints, they default to generic patterns that may not fit your use case.
Outdated Best Practices
AI training data inevitably lags behind current best practices. Security vulnerabilities, deprecated APIs, and outdated frameworks frequently appear in AI-generated code because the model hasn’t been updated with recent developments.
Missing Domain Knowledge
Generic AI responses don’t account for your specific business logic, data models, or integration requirements. This leads to implementations that work in isolation but fail when integrated with your existing systems.
Research-First Methodology: The Solution
The key to better AI coding lies in structured workflows that enforce research before implementation. Instead of asking AI to “create user authentication,” successful developers first establish context through research phases.
How Todo2 Enforces Better AI Workflows
Todo2, a Model Context Protocol (MCP) extension for Cursor AI editor, addresses these issues through its mandatory research workflow:
- Task Creation: Break down complex requests into specific, well-defined todos
- Research Phase: AI must research current best practices and analyze existing codebase patterns before implementation
- Context-Aware Implementation: Code generation happens with full awareness of project constraints and current standards
- Documentation: AI documents reasoning and decisions for team learning
This structured approach transforms AI from a code generator into an informed development partner.
The MCP Advantage
By integrating directly with Cursor through the Model Context Protocol, Todo2 ensures research happens within your development environment. AI can analyze your existing codebase, understand your patterns, and research current best practices - all without context switching.
Code Quality Comparisons
Without Research Structure
// Generic AI response
function authenticateUser(username, password) {
const user = users.find(u => u.username === username);
if (user && user.password === password) {
return { success: true, user };
}
return { success: false };
}This code compiles but has obvious security flaws: plain text password comparison, no rate limiting, and missing error handling.
With Todo2 Research-First Approach
// Research-informed implementation
const bcrypt = require('bcrypt');
const rateLimit = require('express-rate-limit');
const authLimiter = rateLimit({
windowMs: 15 * 60 * 1000, // 15 minutes
max: 5 // limit each IP to 5 requests per windowMs
});
async function authenticateUser(username, password, req) {
try {
// Input validation
if (!username || !password) {
throw new Error('Missing credentials');
}
// Secure database query
const user = await User.findOne({
username: username.toLowerCase(),
isActive: true
});
if (!user) {
// Prevent timing attacks
await bcrypt.hash('dummy', 10);
throw new Error('Invalid credentials');
}
// Secure password verification
const isValid = await bcrypt.compare(password, user.passwordHash);
if (!isValid) {
await user.incrementFailedAttempts();
throw new Error('Invalid credentials');
}
// Reset failed attempts on success
await user.resetFailedAttempts();
return { success: true, user: user.safeData() };
} catch (error) {
console.error('Authentication error:', error.message);
return { success: false, error: 'Authentication failed' };
}
}The research-informed version includes proper password hashing, rate limiting, input validation, and security best practices - because AI researched current authentication standards before implementation.
Learning from AI Comments
One of Todo2’s most powerful features is its comment system. When AI researches a solution, it documents:
- Research findings: Links to current best practices and security guidelines
- Implementation decisions: Why specific approaches were chosen
- Lessons learned: Insights that apply to future similar tasks
This creates a knowledge base that prevents repeating the same mistakes and helps teams understand the reasoning behind architectural decisions.
The Compound Learning Effect
Unlike traditional AI interactions that reset with each conversation, Todo2’s comment system builds institutional knowledge. Each research phase adds to your team’s understanding, creating better context for future AI interactions.
Breaking the Cycle
The difference between frustrating AI coding and productive AI collaboration comes down to structure. Tools like Todo2 provide that structure by:
- Enforcing research phases before implementation
- Maintaining project context across sessions
- Documenting AI reasoning for team learning
- Integrating with your existing workflow in Cursor
Instead of fighting with generic AI responses, you get context-aware implementations backed by current best practices and tailored to your specific project needs.
The Path Forward
AI coding assistants aren’t going anywhere - they’re becoming more powerful and prevalent. The developers who succeed are those who learn to collaborate with AI effectively, not just use it as an advanced autocomplete.
By implementing structured workflows that prioritize research and context, you transform AI from a source of frustration into a genuine productivity multiplier. The key is giving AI the information it needs to make informed decisions, not just hoping it guesses correctly.
Ready to transform your AI coding workflow? Learn more about Todo2 and how it brings structure to AI-assisted development in Cursor.