Prompt Design Sensitivity Analysis

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Understanding Sensitivity Analysis Techniques

When building models and making decisions, it's necessary to account for uncertainty in input variables and assumptions. Sensitivity analysis allows you to systematically test how changes in inputs impact outputs and conclusions.

How to do it

1. Prompt order sensitivity:

You could change the order of the example inputs in the prompt and see how it impacts the model's predictions on a test set. This would reveal if the model is overly sensitive to the ordering of prompts.

2. Quantifying prompt format sensitivity:

Using a tool like FormatSpread, you could rapidly test many different formats for constructing the prompt (e.g. varying the number of examples, using headings, adjusting whitespace.) This would quantify the full range of performance the model exhibits across various prompt formats.

3. Table-driven prompt design:

You could construct prompts in a table-driven way, systematically combining different prompt headers, example inputs, output descriptions etc. This would allow identifying specific prompt component combinations that lead to more robust and consistent performance.

By focusing on these three techniques - analyzing prompt order effects, quantifying sensitivity to format changes, and taking a table-driven approach to systematically construct prompts - you can get a handle on the key sources of prompt sensitivity. This allows you to engineer prompts that lead to better generalization for your specific use case.

Digging deeper into prompt design with Sensitivity Analysis

One-Way Sensitivity Analysis

The simplest approach is one-way or single-parameter sensitivity analysis. You vary one input variable at a time across a range of values while holding all other inputs constant. This isolated testing reveals how sensitive your output is to changes in that single variable.

For example, in a financial model, you could adjust the projected revenue growth rate from 2% to 5% to see how it impacts your profitability while keeping other inputs like costs and tax rates fixed.

Two-Way Sensitivity Analysis

Two-way sensitivity analysis extends the one-way approach by varying two input variables simultaneously. This accounts for any interaction effects between the two variables that could amplify or dampen the impact on the output.

Continuing the financial model example, you could simultaneously adjust both the revenue growth rate and cost inflation rate assumptions to understand their combined effect on projected profits.

Scenario Analysis

With scenario analysis, you define multiple scenarios with different combinations of input values representing various plausible future states or assumption sets. This provides a more holistic view by bundling interrelated variables into scenarios rather than testing them in isolation.

For instance, you could model an optimistic scenario with high-quality training data and a large model size, a pessimistic scenario with low-quality data and a small model size, and a base case scenario with moderate assumptions. This would help you understand how your prompt design performs under different conditions.

While one-way and two-way sensitivity analysis offers simplicity, scenario analysis captures realistic combinatorial effects missed when varying inputs individually.

By applying these sensitivity analysis techniques to your prompt design process, you can create more robust and reliable models that perform well across a range of input conditions and assumptions.