The AI Wasn't Hallucinating. Our Architecture Was.

Short summary

An engineer building an AI validation engine discovered that production failures weren't caused by the model or prompts — they were caused by architecture that forced the LLM to infer information the system already knew. The fix was restructuring so the application handled discovery and metadata collection, making the LLM one component instead of the entire system. The key takeaway: good AI architecture reduces the decisions a model must make, while bad architecture asks the model to compensate for missing system design.

• •Production AI failures often stem from architecture, not model choice or prompts
• •Redesign so the application handles discovery and context structuring before the LLM sees data
• •Good architecture minimizes model decisions; bad architecture asks the model to guess

Generated with AI, which can make mistakes.