diff --git a/examples/offline_inference/disaggregated-prefill-v1/README.md b/examples/offline_inference/disaggregated-prefill-v1/README.md
index f708eb25383..9cbdb19820f 100644
--- a/examples/offline_inference/disaggregated-prefill-v1/README.md
+++ b/examples/offline_inference/disaggregated-prefill-v1/README.md
@@ -5,5 +5,6 @@ This example contains scripts that demonstrate disaggregated prefill in the offl
 ## Files
 
 - `run.sh` - A helper script that will run `prefill_example.py` and `decode_example.py` sequentially.
+  - Make sure you are in the `examples/offline_inference/disaggregated-prefill-v1` directory before running `run.sh`.
 - `prefill_example.py` - A script which performs prefill only, saving the KV state to the `local_storage` directory and the prompts to `output.txt`.
 - `decode_example.py` - A script which performs decode only, loading the KV state from the `local_storage` directory and the prompts from `output.txt`.
diff --git a/examples/offline_inference/disaggregated-prefill-v1/decode_example.py b/examples/offline_inference/disaggregated-prefill-v1/decode_example.py
index 11918f72fee..531c96f176a 100644
--- a/examples/offline_inference/disaggregated-prefill-v1/decode_example.py
+++ b/examples/offline_inference/disaggregated-prefill-v1/decode_example.py
@@ -3,35 +3,47 @@
 from vllm import LLM, SamplingParams
 from vllm.config import KVTransferConfig
 
-# Read prompts from output.txt
-prompts = []
-try:
-    with open("output.txt") as f:
-        for line in f:
-            prompts.append(line.strip())
-    print(f"Loaded {len(prompts)} prompts from output.txt")
-except FileNotFoundError:
-    print("Error: output.txt file not found")
-    exit(-1)
-
-sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
-
-llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct",
-          enforce_eager=True,
-          gpu_memory_utilization=0.8,
-          max_num_batched_tokens=64,
-          max_num_seqs=16,
-          kv_transfer_config=KVTransferConfig(
-              kv_connector="SharedStorageConnector",
-              kv_role="kv_both",
-              kv_connector_extra_config={
-                  "shared_storage_path": "local_storage"
-              }))  #, max_model_len=2048, max_num_batched_tokens=2048)
-
-# 1ST generation (prefill instance)
-outputs = llm.generate(prompts, sampling_params)
-
-for output in outputs:
-    prompt = output.prompt
-    generated_text = output.outputs[0].text
-    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+def read_prompts():
+    """Read prompts from output.txt"""
+    prompts = []
+    try:
+        with open("output.txt") as f:
+            for line in f:
+                prompts.append(line.strip())
+        print(f"Loaded {len(prompts)} prompts from output.txt")
+        return prompts
+    except FileNotFoundError:
+        print("Error: output.txt file not found")
+        exit(-1)
+
+
+def main():
+    prompts = read_prompts()
+    sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
+
+    llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct",
+              enforce_eager=True,
+              gpu_memory_utilization=0.8,
+              max_num_batched_tokens=64,
+              max_num_seqs=16,
+              kv_transfer_config=KVTransferConfig(
+                  kv_connector="SharedStorageConnector",
+                  kv_role="kv_both",
+                  kv_connector_extra_config={
+                      "shared_storage_path": "local_storage"
+                  }))  #, max_model_len=2048, max_num_batched_tokens=2048)
+
+    # 1ST generation (prefill instance)
+    outputs = llm.generate(prompts, sampling_params)
+
+    print("-" * 30)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
+        print("-" * 30)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/offline_inference/disaggregated-prefill-v1/prefill_example.py b/examples/offline_inference/disaggregated-prefill-v1/prefill_example.py
index 798128301e0..24b7b1d8fdb 100644
--- a/examples/offline_inference/disaggregated-prefill-v1/prefill_example.py
+++ b/examples/offline_inference/disaggregated-prefill-v1/prefill_example.py
@@ -3,42 +3,54 @@
 from vllm import LLM, SamplingParams
 from vllm.config import KVTransferConfig
 
-context = "Hi " * 1000
-context2 = "Hey " * 500
-prompts = [
-    context + "Hello, my name is",
-    context + "The capital of France is",
-    context2 + "Your name is",
-    context2 + "The capital of China is",
-]
-
-sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1)
-
-llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct",
-          enforce_eager=True,
-          gpu_memory_utilization=0.8,
-          kv_transfer_config=KVTransferConfig(
-              kv_connector="SharedStorageConnector",
-              kv_role="kv_both",
-              kv_connector_extra_config={
-                  "shared_storage_path": "local_storage"
-              }))  #, max_model_len=2048, max_num_batched_tokens=2048)
-
-# 1ST generation (prefill instance)
-outputs = llm.generate(
-    prompts,
-    sampling_params,
-)
-
-new_prompts = []
-for output in outputs:
-    prompt = output.prompt
-    generated_text = output.outputs[0].text
-    new_prompts.append(prompt + generated_text)
-    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
-
-# Write new_prompts to output.txt
-with open("output.txt", "w") as f:
-    for prompt in new_prompts:
-        f.write(prompt + "\n")
-print(f"Saved {len(new_prompts)} prompts to output.txt")
+
+def read_prompts():
+    context = "Hi " * 1000
+    context2 = "Hey " * 500
+    return [
+        context + "Hello, my name is",
+        context + "The capital of France is",
+        context2 + "Your name is",
+        context2 + "The capital of China is",
+    ]
+
+
+def main():
+    prompts = read_prompts()
+
+    sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1)
+
+    llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct",
+              enforce_eager=True,
+              gpu_memory_utilization=0.8,
+              kv_transfer_config=KVTransferConfig(
+                  kv_connector="SharedStorageConnector",
+                  kv_role="kv_both",
+                  kv_connector_extra_config={
+                      "shared_storage_path": "local_storage"
+                  }))  #, max_model_len=2048, max_num_batched_tokens=2048)
+
+    # 1ST generation (prefill instance)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+    )
+
+    new_prompts = []
+    print("-" * 30)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        new_prompts.append(prompt + generated_text)
+        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
+        print("-" * 30)
+
+    # Write new_prompts to output.txt
+    with open("output.txt", "w") as f:
+        for prompt in new_prompts:
+            f.write(prompt + "\n")
+    print(f"Saved {len(new_prompts)} prompts to output.txt")
+
+
+if __name__ == "__main__":
+    main()