diff --git a/src/cpp/src/llm_pipeline.cpp b/src/cpp/src/llm_pipeline.cpp
index ca01e8e11c..a1fc672a1a 100644
--- a/src/cpp/src/llm_pipeline.cpp
+++ b/src/cpp/src/llm_pipeline.cpp
@@ -31,7 +31,7 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
     ov::InferRequest m_model_runner;
 
     bool is_chat_conversation = false;
-    bool m_is_cache_empty = true;
+    bool m_history_available = false;
     std::optional<int32_t> m_selected_beam = std::nullopt;
     ChatHistory m_history;
     std::string m_templated_chat_history = {};
@@ -112,11 +112,11 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
                 auto new_templated_chat_history  = m_tokenizer.apply_chat_template(m_history, add_generation_prompt);
                 // Do not add special tokens in chat scenario to be aligned with HF.
                 auto new_chat_tokens = m_tokenizer.encode(new_templated_chat_history, ov::genai::add_special_tokens(false));
-                if (m_is_cache_empty) {
-                    encoded_input = new_chat_tokens;
-                } else {
+                if (m_history_available) {
                     auto prev_chat_tokens = m_tokenizer.encode(m_templated_chat_history, ov::genai::add_special_tokens(false));
                     encoded_input = utils::subtract_chat_tokenized_inputs(new_chat_tokens, prev_chat_tokens);
+                } else {
+                    encoded_input = new_chat_tokens;
                 }
                 m_templated_chat_history = new_templated_chat_history;
                 // TODO: Forbid LoRA config change if we are in the chat mode, because it requires regenerating the history with LoRA applied
@@ -218,12 +218,15 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
             std::copy(input_ids.data<int64_t>(), input_ids.data<int64_t>() + input_ids.get_size(), std::back_inserter(m_tokenized_chat_history));
         }
         ov::Tensor tokenized_chat_history = ov::Tensor(ov::element::i64, {1, m_tokenized_chat_history.size()}, m_tokenized_chat_history.data());
-        bool kv_history_available = m_selected_beam.has_value();
+        bool is_cache_empty = !m_selected_beam.has_value();
 
         size_t kv_cache_len = 0;
         ov::Tensor concatenated_attention_mask;
-        if (is_chat_conversation && !m_is_cache_empty) {
-            if (kv_history_available) {
+        if (is_chat_conversation && m_history_available) {
+            if (is_cache_empty) {
+                attention_mask = ov::genai::utils::init_attention_mask(tokenized_chat_history);
+                concatenated_attention_mask = attention_mask;
+            } else {
                 OPENVINO_ASSERT(batch_size == 1, "continuation of generation is possible only for batch 1");
                 // If history is saved in KV cache, concatenate new attention_mask with the already existing.
                 // Between subsequent runs attention_mask should not be modified.
@@ -238,9 +241,6 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
                 std::copy(attention_mask.data<int64_t>(), attention_mask.data<int64_t>() + prompt_len,
                         new_atten_mask.data<int64_t>() + kv_cache_len);
                 concatenated_attention_mask = new_atten_mask;
-            } else {
-                attention_mask = ov::genai::utils::init_attention_mask(tokenized_chat_history);
-                concatenated_attention_mask = attention_mask;
             }
         } else {
             concatenated_attention_mask = attention_mask;
@@ -249,7 +249,7 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
         bool position_ids_available = (num_inputs == 4);
         std::optional<ov::Tensor> position_ids = std::nullopt;
         if (position_ids_available) {
-            if (is_chat_conversation && !kv_history_available) {
+            if (is_chat_conversation && is_cache_empty) {
                 position_ids = ov::Tensor{ov::element::i64, tokenized_chat_history.get_shape()};
             } else {
                 position_ids = ov::Tensor{ov::element::i64, input_ids.get_shape()};
@@ -285,7 +285,7 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
         Sampler sampler = Sampler(m_tokenizer);
         // we can't properly refer to history in case of chat scenario with beam search, so reset_kv_state and use the whole history for each new propmt
         auto input_tokens = input_ids;
-        if (is_chat_conversation && !kv_history_available) {
+        if (is_chat_conversation && is_cache_empty) {
             input_tokens = tokenized_chat_history;
         }
         result = ov::genai::get_lm_encoded_results(m_model_runner, input_tokens, concatenated_attention_mask, streamer_ptr, sampler, requests, position_ids, std::nullopt);
@@ -297,7 +297,7 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
         }
 
         if (is_chat_conversation) {
-            m_is_cache_empty = false;
+            m_history_available = true;
         }
 
         if (is_chat_conversation) {
@@ -320,9 +320,9 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
     void start_chat(const std::string& system_message) override {
         is_chat_conversation = true;
         m_selected_beam = std::nullopt;
-        if (!m_is_cache_empty) {
+        if (m_history_available) {
             reset_kv_state();
-            m_is_cache_empty = true;
+            m_history_available = false;
             m_history = {};
             m_templated_chat_history = "";
             m_tokenized_chat_history = {};
@@ -339,9 +339,9 @@ class StatefulLLMPipeline final : public LLMPipelineImplBase {
     void finish_chat() override {
         is_chat_conversation = false;
         m_selected_beam = std::nullopt;
-        if (!m_is_cache_empty) {
+        if (m_history_available) {
             reset_kv_state();
-            m_is_cache_empty = true;
+            m_history_available = false;
             m_history.clear();
             m_templated_chat_history.clear();
             m_tokenized_chat_history = {};