AI Stabilizer Playground

Step 0 Setup

View Ready-Made Runs

Explore pre-recorded analysis of complex scenarios without real LLM latency.

Go Live

Run real-time inference using active LLM models.

Load predefined test template

...or start with a clean slate

Start entirely from scratch without any predefined formats.

Get started by loading a template or custom setup

Step 1 Compile stabilizer function

Extraction Prompt

Output format

Invalid JSON format

Training Data (up to 20 sets of Source Text + Expected Output)

Invalid JSON format

Prompt variation strategy

Number of prompt variations to compile

Step 2 Extract using stabilizer

Number of responses to sample

Input Document (Source Text)

Variance level: 0 / 5

Generated 0 results with identical settings (using ...).

A low variance score (0-1) means the results were already consistent with each other, even without the stabilizer function.

A medium variance score (2-3) means the results were somewhat inconsistent with each other, and the stabilizer function was needed.

A high variance score (4-5) means the results were very inconsistent with each other. This usually indicates that either the prompt needs work, or the nature of the extraction is non-deterministic.

View Results As

Step 3 Measure Consistency

Compare the best result from each extracted batch to determine overall stabilizer stability. Requires at least 3 extraction runs.

(0/3 runs recorded)

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Compiled Stabilizers

AI Stabilizer Playground

Step 0 Setup

Load predefined test template

...or start with a clean slate

Step 1 Compile stabilizer function

Step 2 Extract using stabilizer

Variance level: 0 / 5

Worst Result

Best Result

Step 3 Measure Consistency

Consistency baseline

Stabilizer-aided Consistency