diff --git a/examples/simple-chat/README.md b/examples/simple-chat/README.md
index 6770a7de0..f0099ce3d 100644
--- a/examples/simple-chat/README.md
+++ b/examples/simple-chat/README.md
@@ -1,7 +1,7 @@
 # llama.cpp/example/simple-chat
 
-The purpose of this example is to demonstrate a minimal usage of llama.cpp to create a simple chat program using the built-in chat template in GGUF files.
+The purpose of this example is to demonstrate a minimal usage of llama.cpp to create a simple chat program using the chat template from the GGUF file.
 
 ```bash
-./llama-simple-chat -m ./models/llama-7b-v2/ggml-model-f16.gguf -c 2048
+./llama-simple-chat -m Meta-Llama-3.1-8B-Instruct.gguf -c 2048
 ...
diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp
index a537425af..389848a83 100644
--- a/examples/simple-chat/simple-chat.cpp
+++ b/examples/simple-chat/simple-chat.cpp
@@ -12,9 +12,7 @@ static void print_usage(int, char ** argv) {
 }
 
 int main(int argc, char ** argv) {
-    // path to the model gguf file
     std::string model_path;
-    // number of layers to offload to the GPU
     int ngl = 99;
     int n_ctx = 2048;
 
@@ -91,13 +89,13 @@ int main(int argc, char ** argv) {
     llama_sampler_chain_add(smpl, llama_sampler_init_temp(0.8f));
     llama_sampler_chain_add(smpl, llama_sampler_init_dist(LLAMA_DEFAULT_SEED));
 
-    // generation helper
+    // helper function to evaluate a prompt and generate a response
     auto generate = [&](const std::string & prompt) {
         std::string response;
 
         // tokenize the prompt
-        const int n_prompt = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
-        std::vector<llama_token> prompt_tokens(n_prompt);
+        const int n_prompt_tokens = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
+        std::vector<llama_token> prompt_tokens(n_prompt_tokens);
         if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
             GGML_ABORT("failed to tokenize the prompt\n");
         }
@@ -106,7 +104,7 @@ int main(int argc, char ** argv) {
         llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
         llama_token new_token_id;
         while (true) {
-            // check if we have enough context space to evaluate this batch
+            // check if we have enough space in the context to evaluate this batch
             int n_ctx = llama_n_ctx(ctx);
             int n_ctx_used = llama_get_kv_cache_used_cells(ctx);
             if (n_ctx_used + batch.n_tokens > n_ctx) {
@@ -116,7 +114,7 @@ int main(int argc, char ** argv) {
             }
 
             if (llama_decode(ctx, batch)) {
-                GGML_ABORT("failed to eval\n");
+                GGML_ABORT("failed to decode\n");
             }
 
             // sample the next token
@@ -127,16 +125,16 @@ int main(int argc, char ** argv) {
                 break;
             }
 
-            // add the token to the response
-            char buf[128];
+            // convert the token to a string, print it and add it to the response
+            char buf[256];
             int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true);
             if (n < 0) {
                 GGML_ABORT("failed to convert token to piece\n");
             }
             std::string piece(buf, n);
-            response += piece;
             printf("%s", piece.c_str());
             fflush(stdout);
+            response += piece;
 
             // prepare the next batch with the sampled token
             batch = llama_batch_get_one(&new_token_id, 1);
@@ -146,34 +144,51 @@ int main(int argc, char ** argv) {
     };
 
     std::vector<llama_chat_message> messages;
-    std::vector<char> formatted(2048);
+    std::vector<char> formatted(llama_n_ctx(ctx));
     int prev_len = 0;
     while (true) {
+        // get user input
+        printf("\033[32m> \033[0m");
         std::string user;
         std::getline(std::cin, user);
-        messages.push_back({"user", strdup(user.c_str())});
 
-        // format the messages
+        if (user.empty()) {
+            break;
+        }
+
+        // add the user input to the message list and format it
+        messages.push_back({"user", strdup(user.c_str())});
         int new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size());
         if (new_len > (int)formatted.size()) {
             formatted.resize(new_len);
             new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size());
         }
+        if (new_len < 0) {
+            fprintf(stderr, "failed to apply the chat template\n");
+            return 1;
+        }
 
-        // remove previous messages and obtain a prompt
+        // remove previous messages to obtain the prompt to generate the response
         std::string prompt(formatted.begin() + prev_len, formatted.begin() + new_len);
 
         // generate a response
-        printf("\033[31m");
+        printf("\033[33m");
         std::string response = generate(prompt);
         printf("\n\033[0m");
 
         // add the response to the messages
         messages.push_back({"assistant", strdup(response.c_str())});
         prev_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), false, formatted.data(), formatted.size());
+        if (prev_len < 0) {
+            fprintf(stderr, "failed to apply the chat template\n");
+            return 1;
+        }
     }
 
-
+    // free resources
+    for (auto & msg : messages) {
+        free(const_cast<char *>(msg.content));
+    }
     llama_sampler_free(smpl);
     llama_free(ctx);
     llama_free_model(model);