Building AI systems that actually work in production requires more than clever algorithms. It demands engineering discipline, systematic thinking, and a commitment to craftsmanship. At Berlin AI Labs, we have developed a set of practices that help us deliver reliable AI solutions for our clients. Today, I want to share the core principles that guide our work.
The Art of Planning: Think Before You Build
Every successful project starts with thoughtful planning. But planning is not just about creating documents that collect dust. It is about deeply understanding the problem before writing a single line of code.
We use what we call the "peer planner" approach. When facing a complex challenge, we first draft a detailed plan. Then we step back and critique it from a different perspective, looking for gaps, risks, and overly optimistic assumptions. Finally, we synthesize both views into a refined plan that acknowledges real world constraints.
This process might seem like extra work upfront, but it saves countless hours of rework down the line. The best code you will ever write is the code you do not have to write because you planned well.
Code Quality: Your Future Self Will Thank You
At the heart of reliable software lies code quality. We believe in two fundamental principles: test first development and leaving code better than you found it.
Test First Is Not Optional
For new features, we write tests before writing the implementation. This is not bureaucracy; it forces clarity. If you cannot write a test for something, you probably do not understand it well enough to build it.
For legacy code, we scaffold tests around existing behavior before making changes. This gives us a safety net and helps us understand what the code actually does, not just what we think it does.
The Clean Code Philosophy
We follow the principles outlined by Robert Martin and Martin Fowler. Small functions that do one thing. Clear, meaningful names. Code that reads like well written prose. These are not luxuries; they are necessities for maintaining systems over time.
We enforce these standards through automated checks: no circular dependencies, low cyclomatic complexity (we aim for three or fewer branches per function), zero linter warnings, and small, focused methods. If a function is trying to do too much, we split it.
Fixing Bugs the Right Way
Everyone encounters bugs. What separates professional engineering from hacking is how you handle them. We follow a strict protocol that ensures bugs are truly fixed, not just patched over, through a rigorous TDD cycle.
1. RED: Reproduce with a Test
First, we write a minimal failing test that reproduces the bug. This is the "red" phase. We run the test to confirm it actually fails. This step is crucial because it proves you understand the problem and provides a base for verification.
2. GREEN: Fix the Code
Next comes "green": we fix the code until the test passes. We apply SOLID and Clean Code principles while doing so. No quick hacks.
3. REFACTOR & Quality Gates
Once the test is green, we improve the structure and readability of the code while keeping all tests green. We run our internal quality gates, ensuring no circular dependencies and keeping cyclomatic complexity at or below 3.
4. REGRESSION: Full Test Suite
We run ALL tests, including unit and prompt tests, to ensure the fix hasn't introduced regressions elsewhere in the system.
5. CODE REVIEW & Verification
An independent review checks for SOLID violations, code smells, and security issues. We also deploy and run E2E tests using Playwright against production whenever possible. If anything is found, we repeat the cycle.
Feature Development: Building with Confidence
New features follow a similar discipline. We start by writing tests that define the expected behavior. This creates a clear specification that both humans and machines can verify.
Then we implement incrementally, making tests pass one at a time. Each passing test is a small victory and a checkpoint we can return to if things go wrong.
After implementation, we apply our refactoring and quality gate process. Finally, we run the full regression test suite to ensure nothing else broke. Only after all checks pass do we commit and push.
Prompt Engineering: Treating Prompts as Code
In AI systems, prompts are the logic we cannot see, but they are logic nonetheless. At Berlin AI Labs, we treat prompts as first-class code. They are versioned, tested, and held to the same craftsmanship standards as our TypeScript or Python code.
1. Define Acceptance Criteria FIRST
Before writing a single line of a prompt, we define exactly what we expect. This includes the output structure (e.g., JSON schema, field presence), content rules (length constraints, specific keywords), and edge-case handling.
2. Write Executable Tests for Prompts
We write tests that validate the prompt's output. These are not just unit tests; they are "semantic" tests that check for structural validation, semantic quality (like hook engagement or simplicity), and resilience to malformed model responses.
3. The Prompt Test Loop
Our workflow follows a strict cycle: write acceptance tests, craft the prompt, validate with real API calls, and only then add it to the codebase. Future changes to any prompt MUST pass all existing tests before they are committed.
4. Versioning and Maintenance
Prompts are versioned just like code. We never edit a prompt without having protect behavior tests in place. This prevents the "silent regression" problem that plagues many AI applications.
This discipline is especially valuable in production systems where prompts may be modified to improve performance. Without tests, it is easy for a well intentioned edit to break an edge case that was working before.
Why This Matters for AI Systems
AI systems are particularly sensitive to engineering quality. They often have subtle bugs that only manifest under specific conditions. They interact with uncertain real world data. They can fail silently in ways that damage trust.
By applying rigorous engineering practices, we catch problems early. Our test suites include edge cases that exercise model behavior in unusual situations. Our code reviews question assumptions about data quality and model reliability. Our quality gates prevent technical debt from accumulating.
The result is AI systems that our clients can rely on. Systems that work correctly not just in demos, but in production, day after day, processing real data from real users.
Continuous Improvement
These practices are not static. We continuously refine our approach based on what we learn. When something goes wrong, we perform a blameless retrospective. We ask what process improvement would have caught this earlier.
We also stay current with industry best practices. The field of software engineering continues to evolve, and staying curious is essential. What worked five years ago may not be the best approach today.
Getting Started
If you are building AI systems, consider adopting these practices step by step. Start with testing: even a few well chosen tests dramatically improve reliability. Add quality gates to your CI/CD pipeline. Introduce code reviews focused on maintainability, not just correctness.
The investment pays dividends over time. Bugs become rarer. Deployments become less stressful. New team members can understand the code faster. The system remains malleable even as it grows more complex.
At Berlin AI Labs, we are always happy to discuss engineering practices with fellow builders. If you are working on AI systems and want to improve your development process, reach out. We love helping teams build software they can be proud of.