diff --git a/cpp/llm_chat.cc b/cpp/llm_chat.cc
index c6758ead1c..46b7fc4117 100644
--- a/cpp/llm_chat.cc
+++ b/cpp/llm_chat.cc
@@ -254,8 +254,8 @@ class LLMChat {
                                           static_cast<int>(kDLCPU), 0,
                                           static_cast<int>(relax_vm::AllocatorType::kPooled));
 
-    encoding_func_ = vm_->GetFunction("prefill");
-    decoding_func_ = vm_->GetFunction("decode");
+    prefill_func_ = vm_->GetFunction("prefill");
+    decode_func_ = vm_->GetFunction("decode");
     encoding_without_cache_func_ = vm_->GetFunction("encoding_without_cache");
     softmax_func_ = vm_->GetFunction("softmax_with_temperature");
     get_metadata_func_ = vm_->GetFunction("get_metadata");
@@ -374,7 +374,7 @@ class LLMChat {
     this->conversation_.Reset();
     this->ResetRuntimeStats();
     this->ResetKVCache();
-    this->filled_kv_cache_len_ = 0;
+    this->total_seq_len_ = 0;
   }
 
   /*! \brief reset the runtime stats. */
@@ -405,7 +405,7 @@ class LLMChat {
     std::vector<int32_t> tokens;
     std::vector<std::string> prompts;
 
-    if (this->filled_kv_cache_len_ == 0) {
+    if (this->total_seq_len_ == 0) {
       prompts = this->conversation_.GetPromptArray();
       if (this->conversation_.add_bos) {
         tokens.insert(tokens.begin(), bos_token_id_);
@@ -417,11 +417,11 @@ class LLMChat {
     std::string all_prompt = GetConcatPrompt(prompts, 0, 0);
     std::vector<int32_t> encoded = this->tokenizer_->Encode(all_prompt);
     tokens.insert(tokens.end(), encoded.begin(), encoded.end());
-    if (this->filled_kv_cache_len_ + tokens.size() + this->mean_gen_len_ < this->max_window_size_) {
+    if (this->total_seq_len_ + tokens.size() + this->mean_gen_len_ < this->max_window_size_) {
       return tokens;
     }
     // need shift window and re-encode
-    this->filled_kv_cache_len_ = 0;
+    this->total_seq_len_ = 0;
     this->ResetKVCache();
     tokens.clear();
     if (this->conversation_.add_bos) {
@@ -505,9 +505,9 @@ class LLMChat {
 
     auto tstart = std::chrono::high_resolution_clock::now();
 
-    int32_t new_seq_len = filled_kv_cache_len_ + token_len;
+    int32_t new_seq_len = total_seq_len_ + token_len;
     NDArray logits_on_device = this->Forward(prompt_tokens, new_seq_len);
-    filled_kv_cache_len_ = new_seq_len;
+    total_seq_len_ = new_seq_len;
 
     int32_t next_token = this->SampleTokenFromLogits(logits_on_device, temperature_, top_p_);
 
@@ -525,8 +525,8 @@ class LLMChat {
 
     auto tstart = std::chrono::high_resolution_clock::now();
 
-    NDArray logits_on_device = this->Forward({last_token}, filled_kv_cache_len_ + 1);
-    filled_kv_cache_len_ += 1;
+    NDArray logits_on_device = this->Forward({last_token}, total_seq_len_ + 1);
+    total_seq_len_ += 1;
 
     int32_t next_token = this->SampleTokenFromLogits(logits_on_device, temperature_, top_p_);
 
@@ -658,12 +658,12 @@ class LLMChat {
           }
           // resize kv to remove the context
           fkvcache_array_popn_(kv_cache_, backoff);
-          filled_kv_cache_len_ -= backoff;
+          total_seq_len_ -= backoff;
         }
       }
     }
     // TODO(mlc-team): add another per convo seq len trigger
-    if (filled_kv_cache_len_ >= max_window_size_) {
+    if (total_seq_len_ >= max_window_size_) {
       stop_triggered_ = true;
     }
     if (stop_triggered_) {
@@ -674,14 +674,15 @@ class LLMChat {
   // run forward compute
   NDArray Forward(std::vector<int32_t> input_tokens, int64_t cur_pos) {
     Array<ObjectRef> ret;
-    if (input_tokens.size() > 1 && encoding_func_.defined()) {
+    if (input_tokens.size() > 1 && prefill_func_.defined()) {
       NDArray input_data = this->GetInputTokenNDArray(input_tokens);
-      ret = encoding_func_(input_data, ShapeTuple({cur_pos}), kv_cache_, params_);
+      ret = prefill_func_(input_data, ShapeTuple({cur_pos}), kv_cache_, params_);
     } else {
+      // running decode function when prefill is not available
       for (int i = 0; i < input_tokens.size(); ++i) {
         NDArray input_data = this->GetInputTokenNDArray({input_tokens[i]});
         int64_t pos = cur_pos + i + 1 - input_tokens.size();
-        ret = decoding_func_(input_data, ShapeTuple({pos}), kv_cache_, params_);
+        ret = decode_func_(input_data, ShapeTuple({pos}), kv_cache_, params_);
       }
     }
     return Downcast<NDArray>(ret[0]);
@@ -780,7 +781,7 @@ class LLMChat {
   // conversation
   Conversation conversation_;
   // total sequence len,
-  int64_t filled_kv_cache_len_{0};
+  int64_t total_seq_len_{0};
   // max window size, mean generation length
   int64_t max_window_size_{768}, mean_gen_len_{128};
   // shift window fill factor
@@ -818,9 +819,9 @@ class LLMChat {
   // The vm module
   Module vm_;
   // encoding function
-  PackedFunc encoding_func_;
+  PackedFunc prefill_func_;
   // decoding function
-  PackedFunc decoding_func_;
+  PackedFunc decode_func_;
   // encoding without cache
   PackedFunc encoding_without_cache_func_;
   // softmax
@@ -968,8 +969,8 @@ class LLMChatModule : public ModuleNode {
         static_cast<int>(relax_vm::AllocatorType::kPooled), static_cast<int>(kDLCPU), 0,
         static_cast<int>(relax_vm::AllocatorType::kPooled));
 
-    chat_->encoding_func_ = chat_->vm_->GetFunction("prefill");
-    chat_->decoding_func_ = chat_->vm_->GetFunction("decode");
+    chat_->prefill_func_ = chat_->vm_->GetFunction("prefill");
+    chat_->decode_func_ = chat_->vm_->GetFunction("decode");
     chat_->encoding_without_cache_func_ = chat_->vm_->GetFunction("encoding_without_cache");
     chat_->softmax_func_ = chat_->vm_->GetFunction("softmax_with_temperature");
     chat_->get_metadata_func_ = chat_->vm_->GetFunction("get_metadata");