> For the complete documentation index, see [llms.txt](https://beyondllm.aiplanet.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://beyondllm.aiplanet.com/core-components/evaluation.md).

# Evaluation

The effectiveness of a RAG pipeline is assessed through four key evaluation benchmarks: Context Relevance, Answer Relevance, Groundedness, and Ground Truth. Each benchmark uses a scoring range from 0 to 10.

## Context Relevance

Measures the relevance of the chunks retrieved by the auto\_retriever in relation to the user's query. Determines the efficiency of the auto\_retriever in fetching contextually relevant information, ensuring that the foundation for generating responses is solid. A score between 0 (least relevant) to 10 (most relevant) evaluates the retriever's performance in sourcing relevant data. 

**Parameters**

* **User Query** :  The Question/Query to get the response of.

**Code snippet**

```python
pipeline = generator.Generate(question=query,retriever=retriever,llm=llm)
print(pipeline.get_context_relevancy())
```

## Answer Relevance 

Evaluates the relevance of the LLM's response to the user query. It assess the LLM's ability to generate useful and appropriate answers, reflecting its utility in practical scenarios. A score from 0 (irrelevant) to 10 (highly relevant) quantifies the relevance of responses to user queries.

**Parameters**

* **User Query** :  The Question/Query to get the response of.

**Code snippet**

```python
pipeline = generator.Generate(question=query,retriever=retriever,llm=llm)
print(pipeline.get_answer_relevancy())
```

## Groundedness 

Determines the extent to which the language model's responses are grounded in the information retrieved by the auto\_retriever, aiming to identify any hallucinated content, it ensures that the outputs are based on factual information. The response is divided into statements which are then cross-referenced with retrieved chunks, scored from 0 (completely hallucinated) to 10 (fully grounded).

**Parameters**

* **User Query** :  The Question/Query to get the response of.

**Code snippet**

```python
pipeline = generator.Generate(question=query,retriever=retriever,llm=llm)
print(pipeline.get_groundedness())
```

## Ground Truth 

Measures the alignment between the LLM's response and a predefined correct answer provided by the user. Evaluates the overall effectiveness of the pipeline in understanding and responding to queries as intended, serving as a comprehensive benchmark of performance. This benchmark considers the entire processing pipeline's ability to produce the expected outcome, with scores reflecting the degree of match to the ground truth answer. A score from 0 to 10 quantifies how well the LLM is performing. 

**Parameters**

* **User Query** :  The Question/Query to get the response of.
* **Ground Truth** : The actual answer to the user query passed earlier. 

**Code snippet**

```python
pipeline = generator.Generate(question=query,retriever=retriever,llm=llm)
print(pipeline.get_ground_truth(ground_truth))
```

## RAG Triad 

Computes and returns the relevancy (Context and Answer) and groundedness scores for the response  generated by the pipeline. This method directly calculates all three key evaluation metrics.

* Context Relevancy
* Answer Relevancy
* Groundedness  

**Code snippet**

```python
pipeline = generator.Generate(question=query,retriever=retriever,llm=llm)
print(pipeline.get_rag_triad_evals())
```