Aura Technologies builds AI for U.S. defense and manufacturing - predictive maintenance for the Army, secured 3D printing for the Navy. After shipping products into production environments, they've documented what actually breaks.
The demo-to-production gap is real. Weekend prototypes handle happy paths. Production systems face edge cases, adversarial inputs, latency constraints, and cost blowouts. Aura's approach: stress-test with adversarial inputs before demos. No exceptions.
Prompt engineering gets treated like real engineering now. Prompts live in version control, go through PR review. Single-word changes have moved accuracy 20% in either direction. This matches what AWS AgentCore users report: guardrails and prompt versioning prevent production failures.
Retrieval quality determines RAG system ceilings. Aura spent months optimizing generation before realizing retrieval was the bottleneck. Now they measure retrieval independently - relevance, recall, precision - before touching generation. Similar to AWS Bedrock users implementing output filtering and observability: you can't optimize what you don't measure.
Caching delivered a 70% API cost reduction in one product. They cache exact matches, semantically similar inputs, and computed embeddings. Essential at production scale, where costs compound.
Error handling is a feature, not an afterthought. AI systems fail unpredictably - timeouts, rate limits, unexpected formats. Aura implements graceful degradation, clear error messages, exponential backoff retries, and fallback behaviors. AgentCore users troubleshooting session ID errors and timeout issues learn this the same way.
The counterintuitive finding: model selection matters least. Data quality, prompt engineering, system design, and UX that guides users toward successful interactions all matter more. GPT-4 versus Claude versus Gemini is increasingly commoditized. A well-designed system with a "worse" model beats poor design with the best model.
Aura holds a $50M Army Research Office contract and recently demonstrated TrustedDM for forward-deployed manufacturing. Their production lessons come from austere environments where failure has consequences. The gap between demo and deployment isn't hype - it's where most AI projects still die.