The conversation around Artificial Intelligence in the public sector has shifted dramatically. Two years ago, it was about possibility. Today, it's about reliability.
The Reliability Gap
In my recent work with [Department Name], we found that while LLMs could generate policy summaries with 90% accuracy, the remaining 10% constituted a critical risk.
"Accuracy is not enough. Explainability is the currency of trust in government."
Key Findings
- RAG is standard, but RAG + Citation is better.
- Human-in-the-loop is not a bottleneck, it's a feature.
- Latency matters less than correctness for high-stakes decisions.
Visualizing the Trade-off
Below is a visualization of Model Size vs. Inference Time vs. Accuracy (Simulated Data).
This demonstrates that we can achieve 95% of the performance with 10% of the compute cost if we optimize our prompts correctly.