> ## Documentation Index > Fetch the complete documentation index at: https://docs.deeprails.com/llms.txt > Use this file to discover all available pages before exploring further. # Instruction Adherence > Prompts used in production workflows often are very complex and structured with dozens of validation rules. The Instruction Adherence metric assesses each of the input rules and evaluates whether the model response followed each consistently. export const DefinitionCard = ({children}) => { return
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; }; export const Scale = ({low, mid, high, lowLabel = "Low", midLabel = "Mid", highLabel = "High", lowDescription, midDescription, highDescription, midColor = "yellow"}) => { return

{low}

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{high}

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{highLabel}

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; }; Instruction Adherence measures how closely a model response follows the instructions defined in the user and/or system prompts. *** ## Understanding Instruction Adherence

How Instruction Adherence Differs from Other Metrics

While it sounds similar at first, Instruction Adherence is distinctly different from other metrics:
Instruction Adherence: Measures whether the response followed how it was supposed to answer—structure, tone, content constraints, formatting, etc.
Context Adherence: Measures whether the response reflects what was in the provided context (e.g., source documents).
Correctness: Measures whether the information in the response is factually accurate, regardless of whether it followed instructions or context.
## Evaluation Process DeepRails performs a Multimodal Partitioned Evaluation of every model output to assess the extent to which it follows all prompt instructions. A few core pieces of logic ensure that the evaluation is as thorough and accurate as possible. The model input is separated into explicit, atomic instructions. The most important claims are intelligently selected for evaluation to ensure the evaluation is completed in a timely manner. The model output is decomposed into segments each relating to an identified instruction. Each segment analyzed and determined to either follow or not follow its instruction. For each binary verdict, a confidence rating is given as well. All claim judgments are weighted by their confidence rating and consolidated into a final instruction adherence score between 0 and 1. ## Addressing Low Instruction Adherence Scores

Improving Instruction Adherence

When models fail to follow instructions, the resulting output may be irrelevant or unusable in later production steps. To improve instruction-following:
Refine prompts: Reword unclear or ambiguous instructions to be more direct, structured, and constraint-based.
Compare model variants: Some models are significantly more instruction-aligned than others. Use Adherence metrics to validate before selecting the model used in deployment.
## Best Practices Categorize instructions (format, tone, scope, etc.) to make it harder for model's to miss them. Write instructions that are structured, unambiguous, and directive (e.g., “Respond only in bullet points” or “Return valid JSON”). While the other metrics ensure the model gives all the right information, Instruction Adherence ensures that it gives it in the right way. This guardrail is essential for structured outputs, enterprise use cases, and task compliance.