Context precision | Opik Documentation

The context precision metric evaluates the accuracy and relevance of an LLM’s response based on provided context, helping to identify potential hallucinations or misalignments with the given information.

How to use the ContextPrecision metric

You can use the ContextPrecision metric as follows:

1 from opik.evaluation.metrics import ContextPrecision
2 
3 metric = ContextPrecision()
4 
5 metric.score(
6     input="What is the capital of France?",
7     output="The capital of France is Paris. It is famous for its iconic Eiffel Tower and rich cultural heritage.",
8     expected_output="Paris",
9     context=["France is a country in Western Europe. Its capital is Paris, which is known for landmarks like the Eiffel Tower."],
10 )

Asynchronous scoring is also supported with the ascore scoring method.

ContextPrecision Prompt

Opik uses an LLM as a Judge to compute context precision, for this we have a prompt template that is used to generate the prompt for the LLM. By default, the gpt-4o model is used to detect hallucinations but you can change this to any model supported by LiteLLM by setting the model parameter. You can learn more about customizing models in the Customize models for LLM as a Judge metrics section.

The template uses a few-shot prompting technique to compute context precision. The template is as follows:

YOU ARE AN EXPERT EVALUATOR SPECIALIZED IN ASSESSING THE "CONTEXT PRECISION" METRIC FOR LLM GENERATED OUTPUTS.
YOUR TASK IS TO EVALUATE HOW PRECISELY A GIVEN ANSWER FROM AN LLM FITS THE EXPECTED ANSWER, GIVEN THE CONTEXT AND USER INPUT.
###INSTRUCTIONS###
1. **EVALUATE THE CONTEXT PRECISION:**
    - **ANALYZE** the provided user input, expected answer, answer from another LLM, and the context.
    - **COMPARE** the answer from the other LLM with the expected answer, focusing on how well it aligns in terms of context, relevance, and accuracy.
    - **ASSIGN A SCORE** from 0.0 to 1.0 based on the following scale:
###SCALE FOR CONTEXT PRECISION METRIC (0.0 - 1.0)###
- **0.0:** COMPLETELY INACCURATE – The LLM's answer is entirely off-topic, irrelevant, or incorrect based on the context and expected answer.
- **0.2:** MOSTLY INACCURATE – The answer contains significant errors, misunderstanding of the context, or is largely irrelevant.
- **0.4:** PARTIALLY ACCURATE – Some correct elements are present, but the answer is incomplete or partially misaligned with the context and expected answer.
- **0.6:** MOSTLY ACCURATE – The answer is generally correct and relevant but may contain minor errors or lack complete precision in aligning with the expected answer.
- **0.8:** HIGHLY ACCURATE – The answer is very close to the expected answer, with only minor discrepancies that do not significantly impact the overall correctness.
- **1.0:** PERFECTLY ACCURATE – The LLM's answer matches the expected answer precisely, with full adherence to the context and no errors.
2. **PROVIDE A REASON FOR THE SCORE:**
    - **JUSTIFY** why the specific score was given, considering the alignment with context, accuracy, relevance, and completeness.
3. **RETURN THE RESULT IN A JSON FORMAT** as follows:
    - `"{VERDICT_KEY}"`: The score between 0.0 and 1.0.
    - `"{REASON_KEY}"`: A detailed explanation of why the score was assigned.
###WHAT NOT TO DO###
- **DO NOT** assign a high score to answers that are off-topic or irrelevant, even if they contain some correct information.
- **DO NOT** give a low score to an answer that is nearly correct but has minor errors or omissions; instead, accurately reflect its alignment with the context.
- **DO NOT** omit the justification for the score; every score must be accompanied by a clear, reasoned explanation.
- **DO NOT** disregard the importance of context when evaluating the precision of the answer.
- **DO NOT** assign scores outside the 0.0 to 1.0 range.
- **DO NOT** return any output format other than JSON.
###FEW-SHOT EXAMPLES###
{examples_str}
NOW, EVALUATE THE PROVIDED INPUTS AND CONTEXT TO DETERMINE THE CONTEXT PRECISION SCORE.
###INPUTS:###
***
Input:
{input}
Output:
{output}
Expected Output:
{expected_output}
Context:
{context}
***

with VERDICT_KEY being context_precision_score and REASON_KEY being reason.