Consistency | Sutro Handbook

Slaying the dragon

In many ways, consistency is the hallmark of analytical AI. We hear it all the time from teams we work with. "Why can't I get my model to consistently get it to behave like I want it to?"

You don't hear this as much from teams who are specifically looking for creative, emergent behavior from models. In the world of analytical AI, your goal is to manufacture boring outcomes: a model that consistently does one thing well. That way you can spend your time on everything that isn't so boring.

The main levers are task specificity, parallel sampling, and better confidence signals.

In This Section

Don't be fooled by determinism Why absolute determinism is less useful than measured consistency for real-world analytical AI systems. Task Specificity How specific task instructions, examples, and edge-case guidance make analytical AI systems more consistent. Fine-tuning and RL When to consider fine-tuning or reinforcement learning for analytical AI tasks, and why prompt optimization should usually come first. Parallel Sampling How parallel model samples and majority voting can improve consistency for repeated analytical AI decisions. Confidence Scores How to use confidence signals, agreement checks, and escalation logic without relying on self-reported model confidence. Ensembles How model ensembles can add useful perspectives, and why they are not always the simplest path to consistent AI behavior. Temperature How to tune model temperature with evals instead of assuming zero temperature is always best for consistency.