Overview

What it is

Shipping LLM changes straight to production often means finding out too late that a model is too slow, too expensive, or that outputs are off—hallucinations, wrong tone, or poor adherence to context. Teams end up guessing which prompt or model is best, or A/B testing in production where mistakes affect real users. We built Prototype so you can test and compare different LLM configurations—models, prompts, parameters—before you deploy. You register your project and version names, run your application with Future AGI instrumentation so requests are traced, attach evaluations to those traces so each run gets scores (e.g. context adherence, tone, safety), and use the Prototype dashboard to compare versions by evaluation results, cost, and latency. You then choose a winner based on that data and promote that configuration to production, so you move from prototype to production with less risk and full observability instead of shipping blindly.

Purpose

• Risk mitigation — Catch hallucinations, bias, or inaccuracies before they reach users.
• Performance optimization — Compare models, prompts, and parameters to find the best configuration.
• Cost efficiency — Test and tune so you deploy a cost-effective setup.
• Evaluations — Use Future AGI evals (e.g. context adherence, tone, safety) on prototype runs to assess quality. Learn more
• Data-driven selection — Choose the winning version from evaluation scores, cost, latency, and other metrics.
• Seamless production transition — Promote the chosen prototype to production with minimal friction and keep observability.

Getting started with prototype