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@@ -20,6 +20,43 @@ RAG evaluates how well a model stays faithful to a provided context when answeri
 - **Temperature**: 0 (to enforce deterministic, grounded outputs)  
 - **System Prompt**: Instructs the model to only use the document and avoid guessing.
 
+### System prompt
+
+This is the system prompt use to generate LLM output for RAG setting:
+
+```
+You are an assistant for question-answering tasks.  
+Given the QUESTION and DOCUMENT you must answer the QUESTION using the information in the DOCUMENT.  
+You must not offer new information beyond the context provided in the DOCUMENT. Do not add any external knowledge.  
+The ANSWER also must not contradict information provided in the DOCUMENT.  
+If the DOCUMENT does not contain the facts to answer the QUESTION or you do not know the answer, you truthfully say that you do not know.  
+You have access to information provided by the user as DOCUMENT to answer the QUESTION, and nothing else.  
+Use three sentences maximum and keep the answer concise.
+```
+
+### Prompt format
+
+Each prompt is formatted as 
+
+```
+DOCUMENT:
+{context}
+
+QUESTION:
+{question}
+```
+
+### Message structure
+
+The models use the following message structure:
+
+```python
+messages = [{"role": "system", "content": system_prompt},
+            {"role": "user", "content": prompt},]
+
+```
+
+
 ## Real-World Knowledge (Non-RAG setting)
 
 This setting evaluates how factually accurate a model is when **no context is provided**. The model must rely solely on its internal knowledge to answer a broad range of user questions across many topics. The answers are then verified using web search to determine factual correctness.
@@ -30,6 +67,19 @@ This setting evaluates how factually accurate a model is when **no context is pr
 - **Temperature**: 1 (to reflect natural, fluent generation)  
 - **System Prompt**: Encourages helpfulness, accuracy, and honesty when unsure.
 
+### System prompt
+
+This is the system prompt use to generate LLM output for Non-RAG setting:
+
+```
+You are a helpful, factual, and concise assistant. 
+Always try to answer the user's question clearly and completely. 
+Do not make up information. If you are unsure or lack the knowledge, say so.
+```
+
+### Message structure
+The message structure for Non-RAG setting is the same as RAG setting.
+
 ---
 
 # Evaluation Method
@@ -46,3 +96,72 @@ Each model's hallucination rate is computed as:
 
 A **lower** hallucination rate indicates **better** performance.
 
+---
+# Example of Hallucination Detection
+
+## RAG Example
+
+Context
+```
+Jonathan Stark (born April 3, 1971) is a former professional tennis player from the United States. 
+During his career he won two Grand Slam doubles titles (the 1994 French Open Men's Doubles and 
+the 1995 Wimbledon Championships Mixed Doubles). He reached the men's singles final at the French Open in 1988, 
+won the French Open men's doubles title in 1984, and helped France win the Davis Cup in 1991.
+```
+
+Prompt
+
+```
+Which tennis player won more Grand Slam titles, Henri Leconte or Jonathan Stark?
+```
+
+LLM Answer
+```
+Jonathan Stark won two Grand Slam titles, while Henri Leconte won four. 
+Therefore, Henri Leconte won more Grand Slam titles.
+```
+
+Verify Label
+```
+Hallucination: Yes
+```
+
+
+Verify Response
+```
+The answer introduces new information about Henri Leconte's Grand Slam titles, 
+which is not present in the document.
+```
+
+## Non-RAG Example
+
+Prompt
+
+```
+What are the ingredients that made the most expensive donut so expensive?
+```
+
+LLM Answer
+```
+The most expensive donut, called "The Golden Donut," was made expensive by several factors:
+
+24-karat gold leaf: Used for decoration.
+
+```
+
+Verify Label
+```
+Hallucination: No
+```
+
+
+Verify Response
+```
+The response mentions 'The Golden Donut' and states that it was made expensive by several factors, 
+including 24-karat gold leaf used for decoration. The search results provide information about expensive donuts, 
+including one called 'The Golden Donut' or similar names, which are associated with luxurious ingredients 
+like 24-karat gold and Cristal champagne. The response correctly identifies 24-karat gold leaf as 
+a factor contributing to the donut's expensiveness, which is supported by multiple search results. 
+While the response simplifies the information, it does not introduce factually incorrect 
+or fabricated details about the donut's ingredients.
+```