Prompt Alignment
This example demonstrates how to use Mastra’s Prompt Alignment metric to evaluate how well responses follow given instructions.
Overview
The example shows how to:
- Configure the Prompt Alignment metric
- Evaluate instruction adherence
- Handle non-applicable instructions
- Calculate alignment scores
Setup
Environment Setup
Make sure to set up your environment variables:
.env
OPENAI_API_KEY=your_api_key_here
Dependencies
Import the necessary dependencies:
src/index.ts
import { openai } from "@ai-sdk/openai";
import { PromptAlignmentMetric } from "@mastra/evals/llm";
Example Usage
Perfect Alignment Example
Evaluate a response that follows all instructions:
src/index.ts
const instructions1 = [
"Use complete sentences",
"Include temperature in Celsius",
"Mention wind conditions",
"State precipitation chance",
];
const metric1 = new PromptAlignmentMetric(openai("gpt-4o-mini"), {
instructions: instructions1,
});
const query1 = "What is the weather like?";
const response1 =
"The temperature is 22 degrees Celsius with moderate winds from the northwest. There is a 30% chance of rain.";
console.log("Example 1 - Perfect Alignment:");
console.log("Instructions:", instructions1);
console.log("Query:", query1);
console.log("Response:", response1);
const result1 = await metric1.measure(query1, response1);
console.log("Metric Result:", {
score: result1.score,
reason: result1.info.reason,
details: result1.info.scoreDetails,
});
// Example Output:
// Metric Result: { score: 1, reason: 'The response follows all instructions.' }
Mixed Alignment Example
Evaluate a response that misses some instructions:
src/index.ts
const instructions2 = [
"Use bullet points",
"Include prices in USD",
"Show stock status",
"Add product descriptions",
];
const metric2 = new PromptAlignmentMetric(openai("gpt-4o-mini"), {
instructions: instructions2,
});
const query2 = "List the available products";
const response2 =
"• Coffee - $4.99 (In Stock)\n• Tea - $3.99\n• Water - $1.99 (Out of Stock)";
console.log("Example 2 - Mixed Alignment:");
console.log("Instructions:", instructions2);
console.log("Query:", query2);
console.log("Response:", response2);
const result2 = await metric2.measure(query2, response2);
console.log("Metric Result:", {
score: result2.score,
reason: result2.info.reason,
details: result2.info.scoreDetails,
});
// Example Output:
// Metric Result: { score: 0.5, reason: 'The response misses some instructions.' }
Non-Applicable Instructions Example
Evaluate a response where instructions don’t apply:
src/index.ts
const instructions3 = [
"Show account balance",
"List recent transactions",
"Display payment history",
];
const metric3 = new PromptAlignmentMetric(openai("gpt-4o-mini"), {
instructions: instructions3,
});
const query3 = "What is the weather like?";
const response3 = "It is sunny and warm outside.";
console.log("Example 3 - N/A Instructions:");
console.log("Instructions:", instructions3);
console.log("Query:", query3);
console.log("Response:", response3);
const result3 = await metric3.measure(query3, response3);
console.log("Metric Result:", {
score: result3.score,
reason: result3.info.reason,
details: result3.info.scoreDetails,
});
// Example Output:
// Metric Result: { score: 0, reason: 'No instructions are followed or are applicable to the query.' }
Understanding the Results
The metric provides:
-
An alignment score between 0 and 1, or -1 for special cases:
- 1.0: Perfect alignment - all applicable instructions followed
- 0.5-0.8: Mixed alignment - some instructions missed
- 0.1-0.4: Poor alignment - most instructions not followed
- 0.0:No alignment - no instructions are applicable or followed
-
Detailed reason for the score, including analysis of:
- Query-response alignment
- Instruction adherence
-
Score details, including breakdown of:
- Followed instructions
- Missed instructions
- Non-applicable instructions
- Reasoning for each instruction’s status
When no instructions are applicable to the context (score: -1), this indicates a prompt design issue rather than a response quality issue.
View Example on GitHub