Smart Branching with Randomized Splits in Neuron

When you're building AI workflows in a Neuron, you’ll eventually need to answer a question every engineer dreads:

How do I test something new without breaking everything else?

Whether you’re A/B testing different models, trying out experimental prompts, or rolling out a risky feature behind a canary deployment, you don’t want to fly blind.

That’s where the Randomized Split node comes in.

In this post, we’ll unpack how to use this powerful tool to inject randomness, gather performance data, and ship safely — all with minimal config and maximum control.

What is the Randomized Split Node?

The Randomized Split node in Neuron acts like a traffic router. Incoming requests are split across two paths (A and B) based on configurable percentages. Think of it as a programmable coin flip, except you get to rig the odds.

Use cases include:

• A/B Testing: Run different prompts, models, or system settings in parallel.
• Canary Deployments: Roll out risky updates to just a slice of users.
• Chaos Engineering: Introduce variability to catch edge cases early.

A/B Testing: GPT-3.5 vs GPT-4

Let’s say you want to compare outputs between two different LLMs.

1. Drop in a Randomized Split node.
2. Set Percentage A to `50`. Percentage B auto-sets to `50`.
3. Connect A Path to a `Call AI Model` node using GPT-3.5.
4. Connect B Path to a `Call AI Model` node using GPT-4.

Now every request has a 50/50 shot of hitting GPT-3.5 or GPT-4. You can log the results, analyze outputs, and let data, not vibes, drive your decision.

Canary Deployments: Playing It Safe

Rolling out a new image generation model? Don’t YOLO it.

1. Add a Randomized Split node.
2. Set Percentage A to `90`. (This means 90% of traffic stays on your stable pipeline.)
3. Route A Path to your existing, trusted workflow.
4. Route B Path to the shiny new one.

This gives you a controlled, low-risk way to introduce changes. As confidence builds, bump B’s traffic share — 10% → 25% → 50% — until you’re ready to go all-in.

Examples of Applications Across Industries

Here’s how teams across different sectors are applying Randomized Split nodes to solve real-world problems inside their AI-driven workflows:

SaaS AI Platform — Prompt Template Testing

A productivity SaaS company uses AI to summarize meetings. They want to evaluate a new prompt that produces more action-oriented summaries.

• How: Route 20% of traffic to the new system prompt.
• What they measure: Summary clarity, user feedback, and usage retention.
• Outcome: New prompt outperformed baseline by 15% and was rolled out globally.

E-Commerce — AI Recommendations

An online retailer is exploring a new recommendation model based on user embeddings.

• How: Use a 90/10 split to test the new engine.
• What they measure: Click-through rates and cart conversions.
• Outcome: 9% boost in add-to-cart rate, leading to broader deployment.

Healthcare — Triage Assistant Testing

A telehealth provider is testing a new LLM-driven symptom checker but needs to avoid errors.

• How: Run the new model on 5% of users, with human review.
• What they measure: Accuracy, safety, and alignment with real diagnoses.
• Outcome: Initial tests were promising, enabling further A/B testing with prompt variations.

Fintech — Fraud Detection Shadow Mode

A fintech company is developing a new fraud model but can’t risk false positives on live transactions.

• How: 1% of traffic is sent to the new model in shadow mode (predictions logged but not enforced).
• What they measure: Alignment with legacy model, fraud capture rate, and false positives.
• Outcome: 40% improvement in fraud detection precision after several weeks of testing.

Pro Tips & Best Practices

• Log everything: Neuron’s execution logs are a goldmine for debugging or to obtain metrics. They tell you which path was taken each time the Neuron runs.
• Release gradually: When releasing new features, start with 5–10% of traffic in path B, while directing most traffic to Path A.
• Measure each path independently: Don’t just compare “what feels better.” Quantify each path's performance and adjust accordingly.
• Layer your splits: Want A/B/C testing? Nest multiple Randomized Split nodes together to create even more scenarios.
• Combine with other nodes: For example, test different system prompts and different models by chaining Randomized split nodes.

TL;DR

The Randomized Split node is your go-to tool for:

• Smart A/B testing with actual traffic
• Safe rollouts via canary deployments
• Injecting controlled chaos into your workflows

It’s simple, powerful, and flexible enough for most testing scenarios you’ll run into in the wild. If you’re not using it yet — now’s a good time to start.

Want to dive deeper? Check out the official documentation here: Randomized Split Node – Neuron Docs