---
title: Instruction Following Evaluator
tags: [safety, prompt-authoring]
sidebar:
  label: "Instruction Following"
---

## Overview

The `InstructionFollowingEvaluator` assesses whether an agent's response follows all explicit instructions provided in the user's prompt. It focuses strictly on instruction compliance — whether specific constraints, requirements, and directives were satisfied — regardless of response quality or factual accuracy.

## Key Features

- **Trace-Level Evaluation**: Evaluates the most recent turn in the conversation
- **Binary Scoring**: Clear Yes (instructions followed) / No (instructions not followed) classification
- **Async Support**: Supports both synchronous and asynchronous evaluation
- **Constraint-Focused**: Evaluates compliance with explicit directives, not overall quality

## When to Use

Use the `InstructionFollowingEvaluator` when you need to:

- Verify that agents respect format, length, or style constraints
- Check compliance with specific answer options or output types
- Assess whether agents follow multi-part instructions completely
- Evaluate instruction adherence independently from correctness

## Evaluation Level

This evaluator operates at the **TRACE_LEVEL**, evaluating the most recent turn in the conversation.

## Parameters

### `model` (optional)
- **Type**: `Model | str | None`
- **Default**: `None` (uses default Bedrock model)
- **Description**: The model to use as the judge.

### `system_prompt` (optional)
- **Type**: `str | None`
- **Default**: `None` (uses built-in template)
- **Description**: Custom system prompt for the judge model.

### `version` (optional)
- **Type**: `str`
- **Default**: `"v0"`
- **Description**: Prompt template version.

## Scoring System

| Rating | Score | Description |
|--------|-------|-------------|
| Yes | 1.0 | All explicit instructions in the prompt are satisfied |
| No | 0.0 | One or more explicit instructions are not satisfied |

A response passes the evaluation if all explicit instructions are followed (score = 1.0).

## What Counts as Explicit Instructions

The evaluator checks for compliance with specific directives such as:

- **Information constraints**: "Based on this text passage, give an overview about [...]"
- **Length requirements**: "Summarize this text in one sentence"
- **Answer options**: "Which of the following is the tallest mountain..."
- **Target audience**: "Write an explanation for middle schoolers"
- **Genre**: "Write an ad for a laundry service"
- **Style**: "Write an ad for a sports car like it's an obituary"
- **Content type**: "Write a body for this email" vs "Write a subject line"

## Evaluation Rules

- If a response includes **more** information than requested, it still passes as long as all requested elements are present
- If a response is purely evasive without any partial or related answer, it defaults to **Yes**
- If a response is partially evasive but provides a partial answer, the partial answer is judged
- If there are **no explicit instructions** in the input (casual or open-ended requests), defaults to **Yes**
- The evaluator does **not** assess factual accuracy, writing quality, or response effectiveness

## Basic Usage

:::caution[Required: Session ID Trace Attributes]
When using `StrandsInMemorySessionMapper`, you **must** include session ID trace attributes in your agent configuration. This prevents spans from different test cases from being mixed together in the memory exporter.
:::

```python
from strands import Agent
from strands_evals import Case, Experiment
from strands_evals.evaluators import InstructionFollowingEvaluator
from strands_evals.mappers import StrandsInMemorySessionMapper
from strands_evals.telemetry import StrandsEvalsTelemetry

telemetry = StrandsEvalsTelemetry().setup_in_memory_exporter()

def task_function(case: Case) -> dict:
    telemetry.in_memory_exporter.clear()
    agent = Agent(
        trace_attributes={"session.id": case.session_id},
        callback_handler=None
    )
    response = agent(case.input)
    spans = telemetry.in_memory_exporter.get_finished_spans()
    mapper = StrandsInMemorySessionMapper()
    session = mapper.map_to_session(spans, session_id=case.session_id)
    return {"output": str(response), "trajectory": session}

cases = [
    Case(
        name="format-constraint",
        input="List the top 3 programming languages in bullet points."
    ),
    Case(
        name="length-constraint",
        input="Explain quantum computing in exactly one sentence."
    ),
]

experiment = Experiment(cases=cases, evaluators=[InstructionFollowingEvaluator()])
reports = experiment.run_evaluations(task_function)
reports[0].run_display()
```

## Combining with Other Evaluators

Pair with quality evaluators to assess both compliance and correctness:

```python
evaluators = [
    InstructionFollowingEvaluator(),  # Did it follow the format/constraints?
    CorrectnessEvaluator(),           # Is the content factually correct?
    ConcisenessEvaluator(),           # Is it appropriately concise?
]
```

## Related Evaluators

- [**CorrectnessEvaluator**](correctness_evaluator.md): Evaluates factual accuracy (complementary to instruction following)
- [**OutputEvaluator**](output_evaluator.md): Flexible custom rubric evaluation
- [**RefusalEvaluator**](refusal_evaluator.md): Detect when agents refuse to address prompts
- [**CoherenceEvaluator**](coherence_evaluator.md): Assess logical consistency and reasoning
