Beyond Prompts: How Orchestre Uses Context Engineering for Smarter AI

Published: June 27, 2025 | 7 min read

"85% of developers report that AI-generated code requires significant modification before it can be used in production." - Stack Overflow Developer Survey 2024

You've experienced it yourself. You ask an AI coding assistant to build a feature, and it generates a block of code. It looks impressive, but when you try to use it, you find it doesn't fit your project's architecture, uses libraries you don't have, and completely ignores your coding style.

This is the core problem with most AI development tools: they are powerful, but they lack context. They're like a brilliant, world-class programmer who has amnesia every five minutes.

At Orchestre, we solve this problem with a discipline we call Complex Context Engineering. It's the secret sauce that transforms a generic AI into a hyper-aware, specialized member of your development team.

For Our Non-Technical Readers: What is Context Engineering?

Imagine you hire a master chef to cook in your kitchen.

A basic approach (like simple prompt engineering) is to just give them a recipe title: "Make coq au vin." They'll make a technically correct dish, but it might not be what you wanted. They might use an ingredient you're allergic to, a pan you don't own, or make a portion size for two when you're hosting twenty.

Context engineering is like giving that chef a full briefing:

• "Here is our detailed recipe book that shows our family's preferred style (existing patterns)."
• "The pantry is stocked with these specific ingredients, and we have these pots and pans available (project dependencies and constraints)."
• "We're cooking for a 20-person dinner party, and Uncle Bob is allergic to mushrooms (user requirements)."
• "Remember, we always sear the chicken before braising for extra flavor (best practices and conventions)."

Suddenly, the chef isn't just cooking; they're creating the perfect meal for your specific situation.

Orchestre is the expert that briefs the chef (the AI), ensuring the final result is exactly what your project needs.

How Orchestre Engineers Context

Orchestre systematically builds a rich, multi-layered context for the AI before it ever writes a line of code. This is achieved through four key pillars.

1. Intelligent Prompts: The "Program" for the AI

The commands you run in Orchestre, like /orchestrate or /execute-task, are not simple instructions. They are sophisticated, multi-step prompts that guide the AI's thinking process.

Instead of saying, "Build a login feature," an Orchestre prompt says:

"First, discover the existing authentication patterns in this specific project. Then, analyze the security requirements for a modern web application. Finally, implement a login feature that is consistent with the discovered patterns and meets the security standards."

This engineers a problem-solving framework for the AI, dramatically narrowing the possibility of error and ensuring the output is relevant.

2. Pattern Discovery: Learning From Your Code

Your codebase is a rich source of context. Orchestre is designed to learn from it. Prompts frequently instruct the AI to analyze your existing code to identify and adopt your conventions.

• Code Style: It matches your formatting, naming, and commenting style.
• Architectural Patterns: If you use a service layer, it generates new code that also uses a service layer.
• Component Structure: It builds new UI components that look and feel like your existing ones.

This makes the AI-generated code feel like it was written by a seasoned member of your team who has been there from day one.

Example: Generic AI vs Context-Aware AI

// Generic AI Output
function createUser(email, password) {
  // Basic implementation
  const user = { email, password };
  database.save(user);
  return user;
}

// Orchestre Context-Aware Output (after pattern discovery)
export const createUser = async (input: CreateUserInput): Promise<UserResponse> => {
  // Follows your validation patterns
  const validated = createUserSchema.parse(input);
  
  // Uses your service layer pattern
  const hashedPassword = await hashingService.hash(validated.password);
  
  // Matches your error handling style
  try {
    const user = await userRepository.create({
      ...validated,
      password: hashedPassword,
    });
    
    // Consistent with your response format
    return formatUserResponse(user);
  } catch (error) {
    throw new ApplicationError('USER_CREATION_FAILED', error);
  }
};

3. Best Practices: The "Rulebook"

Orchestre comes with built-in knowledge of industry best practices, encoded into its templates and prompts.

• The MakerKit SaaS template understands multi-tenancy and subscription billing logic.
• The Cloudflare template knows the constraints and opportunities of edge computing.
• The /security-audit prompt is guided by principles from the OWASP Top 10.

This provides a foundational layer of expert knowledge, ensuring the AI's output is not just functional but also secure, scalable, and maintainable.

4. Persistent Memory: The "Project Brain"

This is perhaps the most powerful aspect of Orchestre's context engineering. Using the native CLAUDE.md file system, Orchestre builds a "brain" for your project.

Every time you use an Orchestre command:

• It reads the relevant CLAUDE.md files to understand past decisions, architectural choices, and team conventions.
• It writes the results of its work back to these files, documenting new patterns, decisions, and implementation details.

This creates a virtuous cycle. The more you use Orchestre, the smarter it gets about your specific project. The context becomes richer and more detailed over time, leading to increasingly accurate and helpful AI assistance.

The Result: Predictable, High-Quality Outcomes

By systematically engineering context, Orchestre transforms AI from a wild, unpredictable force into a reliable partner. The benefits are clear:

• Consistency: Code generated today matches the code written six months ago.
• Reliability: The AI builds on proven patterns from your own codebase, not generic examples from the internet.
• Maintainability: The output is clean, well-documented, and easy for human developers to understand and extend.
• Speed: You spend less time fixing, refactoring, and explaining, and more time building.

Context Engineering in Action: Key Commands

This all sounds great in theory, but how does it work in practice? Here are a few of Orchestre's core commands and how they act as powerful context engineers:

/orchestrate

This is the master planner. When you provide your project requirements, it doesn't just generate a generic plan. It first discovers your project's existing architecture and patterns, then creates a step-by-step plan that is contextually aware of your specific technology stack and goals.

/discover-context

This is context engineering made explicit. This command dives deep into your codebase to analyze and document its structure, patterns, and dependencies. The output is then saved to your project's memory, enriching the context for all future AI interactions.

/execute-task

When you ask Orchestre to implement a feature, it doesn't do so in a vacuum. It first performs a micro-discovery phase to understand the immediate context of the code it's about to write, ensuring the new code fits seamlessly with the old.

/review

A code review is only as good as the reviewer's context. Orchestre's multi-LLM review process engineers context by combining multiple, diverse perspectives (security, performance, architecture) into a single, consensus-driven report.

/generate-implementation-tutorial

Our newest command that exemplifies context engineering at its finest. Read more about it in our detailed announcement post.

Where to Start Your Journey

Ready to see context engineering in action? Here are some great places to start:

• Tutorial: Your First Project: A hands-on guide to creating your first application and experiencing the context-aware workflow firsthand.
• Tutorial: Building a SaaS with MakerKit: See how Orchestre leverages the deep context of a professional template to build a complete SaaS application.
• Explore the Full Learning Path: Follow our structured tutorials to go from beginner to expert.

Conclusion: The Future is Contextual

The next frontier in AI-assisted development isn't just about building more powerful models; it's about making those models more effective. Complex Context Engineering is the key to unlocking that potential.

Orchestre provides the framework to do just that. It creates a rich, dynamic, and persistent information environment where AI can thrive, moving beyond simple code generation to become a true partner in building high-quality, production-ready software.

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Ready to see what a context-aware AI can do for your project? Get started with Orchestre today.