diff --git a/examples/offline_inference/structured_outputs.py b/examples/offline_inference/structured_outputs.py
index 5ec4dbe6..363b500e 100644
--- a/examples/offline_inference/structured_outputs.py
+++ b/examples/offline_inference/structured_outputs.py
@@ -1,4 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
+"""
+This file demonstrates the example usage of guided decoding 
+to generate structured outputs using vLLM. It shows how to apply 
+different guided decoding techniques such as Choice, Regex, JSON schema, 
+and Grammar to produce structured and formatted results 
+based on specific prompts.
+"""
 
 from enum import Enum
 
@@ -7,26 +14,21 @@ from pydantic import BaseModel
 from vllm import LLM, SamplingParams
 from vllm.sampling_params import GuidedDecodingParams
 
-llm = LLM(model="Qwen/Qwen2.5-3B-Instruct", max_model_len=100)
-
 # Guided decoding by Choice (list of possible options)
-guided_decoding_params = GuidedDecodingParams(choice=["Positive", "Negative"])
-sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
-outputs = llm.generate(
-    prompts="Classify this sentiment: vLLM is wonderful!",
-    sampling_params=sampling_params,
-)
-print(outputs[0].outputs[0].text)
+guided_decoding_params_choice = GuidedDecodingParams(
+    choice=["Positive", "Negative"])
+sampling_params_choice = SamplingParams(
+    guided_decoding=guided_decoding_params_choice)
+prompt_choice = "Classify this sentiment: vLLM is wonderful!"
 
 # Guided decoding by Regex
-guided_decoding_params = GuidedDecodingParams(regex=r"\w+@\w+\.com\n")
-sampling_params = SamplingParams(guided_decoding=guided_decoding_params,
-                                 stop=["\n"])
-prompt = ("Generate an email address for Alan Turing, who works in Enigma."
-          "End in .com and new line. Example result:"
-          "alan.turing@enigma.com\n")
-outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
-print(outputs[0].outputs[0].text)
+guided_decoding_params_regex = GuidedDecodingParams(regex=r"\w+@\w+\.com\n")
+sampling_params_regex = SamplingParams(
+    guided_decoding=guided_decoding_params_regex, stop=["\n"])
+prompt_regex = (
+    "Generate an email address for Alan Turing, who works in Enigma."
+    "End in .com and new line. Example result:"
+    "alan.turing@enigma.com\n")
 
 
 # Guided decoding by JSON using Pydantic schema
@@ -44,16 +46,11 @@ class CarDescription(BaseModel):
 
 
 json_schema = CarDescription.model_json_schema()
-
-guided_decoding_params = GuidedDecodingParams(json=json_schema)
-sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
-prompt = ("Generate a JSON with the brand, model and car_type of"
-          "the most iconic car from the 90's")
-outputs = llm.generate(
-    prompts=prompt,
-    sampling_params=sampling_params,
-)
-print(outputs[0].outputs[0].text)
+guided_decoding_params_json = GuidedDecodingParams(json=json_schema)
+sampling_params_json = SamplingParams(
+    guided_decoding=guided_decoding_params_json)
+prompt_json = ("Generate a JSON with the brand, model and car_type of"
+               "the most iconic car from the 90's")
 
 # Guided decoding by Grammar
 simplified_sql_grammar = """
@@ -64,12 +61,39 @@ table ::= "table_1 " | "table_2 "
 condition ::= column "= " number
 number ::= "1 " | "2 "
 """
-guided_decoding_params = GuidedDecodingParams(grammar=simplified_sql_grammar)
-sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
-prompt = ("Generate an SQL query to show the 'username' and 'email'"
-          "from the 'users' table.")
-outputs = llm.generate(
-    prompts=prompt,
-    sampling_params=sampling_params,
-)
-print(outputs[0].outputs[0].text)
+guided_decoding_params_grammar = GuidedDecodingParams(
+    grammar=simplified_sql_grammar)
+sampling_params_grammar = SamplingParams(
+    guided_decoding=guided_decoding_params_grammar)
+prompt_grammar = ("Generate an SQL query to show the 'username' and 'email'"
+                  "from the 'users' table.")
+
+
+def format_output(title: str, output: str):
+    print(f"{'-' * 50}\n{title}: {output}\n{'-' * 50}")
+
+
+def generate_output(prompt: str, sampling_params: SamplingParams, llm: LLM):
+    outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
+    return outputs[0].outputs[0].text
+
+
+def main():
+    llm = LLM(model="Qwen/Qwen2.5-3B-Instruct", max_model_len=100)
+
+    choice_output = generate_output(prompt_choice, sampling_params_choice, llm)
+    format_output("Guided decoding by Choice", choice_output)
+
+    regex_output = generate_output(prompt_regex, sampling_params_regex, llm)
+    format_output("Guided decoding by Regex", regex_output)
+
+    json_output = generate_output(prompt_json, sampling_params_json, llm)
+    format_output("Guided decoding by JSON", json_output)
+
+    grammar_output = generate_output(prompt_grammar, sampling_params_grammar,
+                                     llm)
+    format_output("Guided decoding by Grammar", grammar_output)
+
+
+if __name__ == "__main__":
+    main()