Building AI Systems That Actually Work

After weeks of experimentation, I’m starting to understand the unique engineering challenges that come with building AI-powered products. Unlike traditional software where the behavior is deterministic, AI systems introduce a level of unpredictability that requires a completely different approach to system design.

The Reliability Challenge

One of the biggest obstacles I’ve encountered is creating AI systems that are reliable enough for production use. Large language models (LLMs) can hallucinate, miss crucial context, or simply respond in unhelpful ways.

To address this, I’ve been implementing several strategies:

1. Structured Output Enforcement: Using JSON schemas and validation to ensure the model outputs follow consistent patterns
2. Fallback Systems: Creating graceful degradation paths when AI components fail
3. Continuous Evaluation: Building automated testing harnesses to catch regressions
4. Human-in-the-Loop Design: Integrating human oversight where appropriate

Data Pipeline Architecture

The data flowing in and out of AI systems requires careful management. I’ve found that a well-designed data pipeline is crucial for both training and inference.

┌───────────────┐     ┌───────────────┐     ┌───────────────┐
│   Raw Data    │────▶│  Processing   │────▶│ Storage Layer │
└───────────────┘     └───────────────┘     └───────────────┘
                                                    │
                                                    ▼
┌───────────────┐     ┌───────────────┐     ┌───────────────┐
│    Model      │◀────│  Feature      │◀────│  Data Access  │
│   Inference   │     │  Engineering  │     │     Layer     │
└───────────────┘     └───────────────┘     └───────────────┘

Cost Optimization

Another major challenge is optimizing costs. AI inference can get expensive quickly, particularly when using the latest models. Here are some approaches I’m using:

• Model Cascading: Using smaller, cheaper models for initial processing, only escalating to larger models when needed
• Response Caching: Storing responses for common queries
• Prompt Optimization: Fine-tuning prompts to reduce token usage without sacrificing quality
• Batch Processing: Grouping requests together when real-time responses aren’t needed

Product Philosophy

From a product perspective, the key insight I’ve gained is that AI should be invisible when it’s working correctly. Users don’t care about the sophisticated models behind the scenes—they care about the problems being solved.

This has led me to focus relentlessly on user experience, making sure the AI capabilities are seamlessly integrated into the workflow.

Next Steps

My next engineering challenge is building a better system for continuous improvement. I’ll be sharing my approach to feedback loops, model fine-tuning, and how to evolve an AI product based on real user interactions.

What engineering challenges have you faced when building AI products? I’d love to hear your experiences and approaches.