> ## 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. # Context Adherence > One of the most powerful aspects of generative AI is models' abilities to adjust to a given context. As such, monitoring the model's adherence is the most critical metric for many use cases. Note, however, the evaluation will not work effectively if DeepRails does not detect enough context in the prompt. Only use Context Adherence when a notable set of context is passed along with the prompt. 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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{lowDescription &&

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{mid &&

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; }; Context Adherence measures how closely an AI response aligns with the information in the context provided in or with the user prompt. **This metric is only appropriate for prompts that include a significant context window.** *** ## Understanding Context Adherence

Context Adherence vs. Other Metrics

Context Adherence is critical in tasks that require strict fidelity to provided documents or sources.
Context Adherence: Measures whether the response reflects the information available in the provided context (e.g., retrieved documents, input references).
Correctness: Measures whether the response is factually accurate based on external truth—regardless of the provided context.
Instruction Adherence: Measures whether the model followed *how* it was supposed to answer, based on explicit instructions (e.g., tone, format).
*** ## Evaluation Process DeepRails performs a Multimodal Partitioned Evaluation of every model output to assess whether each claim is grounded in the context of the input to the model. Since adherence metrics involve more analysis of the model input than metrics like Correctness, the evaluation flow is a bit more complex. ```mermaid theme={null} flowchart LR A["Context Identification"] B["Claim Identification"] C1["Insufficient Context"] C2["Claim/Context Matching"] D["Evaluation and Y/N Verdict Assignment"] E["Confidence Assignment"] F["Score Aggregation"] A --> B B --> C1 B --> C2 C2 --> D D --> E E --> F ``` Context Adherence evaluations will terminate prematurely if insufficient context is detected, and the output will be given a 100% score as a default. *** ## Addressing Low Context Adherence Scores

Improving Context Adherence

To reduce out-of-context generations and improve grounding quality:
Use clear and complete context: You should ensure that all facts needed for an ideal response are included in the context window.
Instruct against extrapolation: You should direct the model to avoid speculation or deviation in all prompts that use a context window.
Audit across prompt types: Compare how different tasks (e.g., summarization, QA) influence context drift, and use different models, prompts, and/or context windows for each.
*** Context Adherence is critical for grounded generation tasks like RAG, search, summarization, and citation. However, it should not be used in context-light applications.