diff --git a/src/plugins/intel_npu/src/plugin/npuw/llm_compiled_model.cpp b/src/plugins/intel_npu/src/plugin/npuw/llm_compiled_model.cpp
index 0728bed8f9f88b..26dcd9821697b4 100644
--- a/src/plugins/intel_npu/src/plugin/npuw/llm_compiled_model.cpp
+++ b/src/plugins/intel_npu/src/plugin/npuw/llm_compiled_model.cpp
@@ -238,7 +238,7 @@ ov::npuw::LLMCompiledModel::LLMCompiledModel(const std::shared_ptr<ov::Model>& m
     std::map<std::string, ov::Any> npuw_llm_props;
     std::map<std::string, ov::Any> other_props;
     split_llm_properties(properties, npuw_llm_props, other_props);
-    
+
     // Remove "NPUW_LLM_PREFILL_CONFIG", "NPUW_LLM_GENERATE_CONFIG" from map,
     // to not pass them into ::intel_npu::Config object, as we don't need to
     // preserve them somewhere.
@@ -280,11 +280,12 @@ ov::npuw::LLMCompiledModel::LLMCompiledModel(const std::shared_ptr<ov::Model>& m
 
     const ::intel_npu::npuw::llm::GenerateHint generate_hint = m_cfg.get<::intel_npu::NPUW_LLM_GENERATE_HINT>();
     LOG_DEBUG("9. Passed GENERATE_HINT: " << std::string(::intel_npu::NPUW_LLM_GENERATE_HINT::toString(generate_hint)));
-     // NB: GENERATE_HINT is only applicable for default generate config!
+    // NB: GENERATE_HINT is only applicable for default generate config!
     if (generate_config_opt.has_value() && npuw_llm_props.count(ov::intel_npu::npuw::llm::generate_hint.name())) {
         OPENVINO_THROW("GENERATE_HINT is only applicable for default generate config!");
     }
-    auto generate_config = opt_or_default(generate_config_opt, get_default_generate_config(model, npudesc, generate_hint));
+    auto generate_config =
+        opt_or_default(generate_config_opt, get_default_generate_config(model, npudesc, generate_hint));
 
     merge_config_with(prefill_config, properties_copy);
     merge_config_with(generate_config, properties_copy);
@@ -310,7 +311,8 @@ void ov::npuw::LLMCompiledModel::set_property(const ov::AnyMap& properties) {
 
 ov::Any ov::npuw::LLMCompiledModel::get_property(const std::string& name) const {
     OPENVINO_SUPPRESS_DEPRECATED_START
-    if (name == ov::intel_npu::npuw::llm::prefill_config.name() || name == ov::intel_npu::npuw::llm::generate_config.name()) {
+    if (name == ov::intel_npu::npuw::llm::prefill_config.name() ||
+        name == ov::intel_npu::npuw::llm::generate_config.name()) {
         OPENVINO_THROW(name, " is write-only option!");
     }
 
diff --git a/src/plugins/intel_npu/src/plugin/npuw/llm_infer_request.cpp b/src/plugins/intel_npu/src/plugin/npuw/llm_infer_request.cpp
index d3147669569b65..a9615b381a6c21 100644
--- a/src/plugins/intel_npu/src/plugin/npuw/llm_infer_request.cpp
+++ b/src/plugins/intel_npu/src/plugin/npuw/llm_infer_request.cpp
@@ -51,7 +51,7 @@ ov::npuw::LLMInferRequest::LLMInferRequest(const std::shared_ptr<ov::npuw::LLMCo
 
 void ov::npuw::LLMInferRequest::prepare_for_new_conversation() {
     fill_tensor<int64_t>(m_prefill_request->get_tensor(m_prefill_in_ports.at("input_ids")),
-         m_npuw_llm_compiled_model->m_cfg.get<::intel_npu::NPUW_LLM_PAD_TOKEN_ID>());
+                         m_npuw_llm_compiled_model->m_cfg.get<::intel_npu::NPUW_LLM_PAD_TOKEN_ID>());
     fill_tensor<int64_t>(m_prefill_request->get_tensor(m_prefill_in_ports.at("attention_mask")), 0);
     fill_tensor<int64_t>(m_prefill_request->get_tensor(m_prefill_in_ports.at("position_ids")), 0);
     fill_tensor<int64_t>(m_kvcache_request->get_tensor(m_kvcache_in_ports.at("attention_mask")), 0);
@@ -114,11 +114,10 @@ void ov::npuw::LLMInferRequest::infer_generate(ov::SoPtr<ov::ITensor> input_ids,
             //        taking into account kvcache dimension.
             fill_tensor<ov::float16>(kvcache_in_tensor, 0);
 
-            auto prefill_out_slice =
-                make_tensor_slice(prefill_out_tensor,
-                                  kvcache_desc.dim,
-                                  kvcache_desc.max_prompt_size - kvcache_desc.num_stored_tokens,
-                                  kvcache_desc.max_prompt_size);
+            auto prefill_out_slice = make_tensor_slice(prefill_out_tensor,
+                                                       kvcache_desc.dim,
+                                                       kvcache_desc.max_prompt_size - kvcache_desc.num_stored_tokens,
+                                                       kvcache_desc.max_prompt_size);
 
             auto kvcache_in_slice =
                 make_tensor_slice(kvcache_in_tensor, kvcache_desc.dim, 0u, kvcache_desc.num_stored_tokens);