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Is your AI routing actually working? Stop guessing and test it the right way.

Name: Is your AI routing actually working? Stop guessing and test it the right way.
Uploaded: 2026-10-01T05:19:31.000Z
Duration: 1 min 28 s
Channel: Julian Goldie Agency
Description: Validating LLM model routing with Jev AI, recommending testing against historical data rather than trusting vendor claims. Model disagreements often point to vague criteria, which can be improved through iterative refinement.

Julian Goldie Agency3 October 2026Watch on YouTube

Part of series

Ep. 12 · AI-routing optimaliseren

Strategieën om AI-modelroutering efficiënter in te richten en tokenkosten te minimaliseren.

View the series

What you'll learn

That relying on vendor claims about LLM model routing is a trap
That you validate routing by testing decisions against historical data
That model disagreements often point to vague criteria rather than broken logic
That iteratively refining criteria based on outputs builds more robust routing
That well-tested routing contributes to reliable and cost-effective systems

Frequently asked questions

Why is it not enough to trust vendor claims about LLM model routing?

According to the video, relying on vendor claims alone is a trap. Instead, you test routing effectively by validating decisions against historical data.

How do you test model routing effectively?

By running the routing against real past decisions instead of trusting the system blind.

What does it mean when models disagree in routing?

Model disagreements often point to vague criteria rather than broken logic. You can then refine the criteria based on those outputs.

What does iteratively refining criteria deliver?

By refining criteria based on outputs, you build robust and cost-effective routing systems that perform reliably.

Topics

llm routing evaluation automation testing

Description from the channel

Want to make money and save time with AI? Join here: https://www.skool.com/ai-profit-lab-7462/about Jev AI is changing how developers handle LLM model routing, but relying on vendor claims alone is a trap. Testing effectively requires validating decisions against historical data rather than just trusting the system blind. When model disagreements occur, they often highlight vague criteria rather than broken logic. By running Jev AI against real past decisions and refining criteria based on those outputs, developers can build robust, cost-effective routing systems that actually perform reliably. #ai #routing #llm #automation #tech