diff --git a/ChatQnA/benchmark/performance/helm_charts/README.md b/ChatQnA/benchmark/performance/helm_charts/README.md
index 2ad932970..3bf47327b 100644
--- a/ChatQnA/benchmark/performance/helm_charts/README.md
+++ b/ChatQnA/benchmark/performance/helm_charts/README.md
@@ -10,29 +10,27 @@ This document guides you through deploying ChatQnA pipelines using Helm charts.
 # on k8s-master node
 cd GenAIExamples/ChatQnA/benchmark/performance/helm_charts
 
-# Replace <your token> with your actual Hugging Face token and run the following command:
-HUGGINGFACE_TOKEN=<your token>
-find . -name '*.yaml' -type f -exec sed -i "s#\${HF_TOKEN}#${HUGGINGFACE_TOKEN}#g" {} \;
-
-# Replace the following placeholders with the desired model IDs:
-LLM_MODEL_ID=Intel/neural-chat-7b-v3-3
-EMBEDDING_MODEL_ID=BAAI/bge-base-en-v1.5
-RERANK_MODEL_ID=BAAI/bge-reranker-base
-find . -name '*.yaml' -type f -exec sed -i "s#\$(LLM_MODEL_ID)#${LLM_MODEL_ID}#g" {} \;
-find . -name '*.yaml' -type f -exec sed -i "s#\$(EMBEDDING_MODEL_ID)#${EMBEDDING_MODEL_ID}#g" {} \;
-find . -name '*.yaml' -type f -exec sed -i "s#\$(RERANK_MODEL_ID)#${RERANK_MODEL_ID}#g" {} \;
-
+# Replace the key of HUGGINGFACEHUB_API_TOKEN with your actual Hugging Face token:
+# vim customize.yaml
+HUGGINGFACEHUB_API_TOKEN: hf_xxxxx
 ```
 
-### ChatQnA Installation
+### Deploy your ChatQnA
 
 ```bash
 # Deploy a ChatQnA pipeline using the specified YAML configuration.
 # To deploy with different configurations, simply provide a different YAML file.
-helm install chatqna helm_charts/ -f helm_charts/oob_single_node.yaml
-
-# Tips: To display rendered manifests according to the given yaml.
-helm template chatqna helm_charts/ -f helm_charts/oob_single_node.yaml
+helm install chatqna helm_charts/ -f customize.yaml
 ```
 
 Notes: The provided [BKC manifests](https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA/benchmark) for single, two, and four node Kubernetes clusters are generated using this tool.
+
+## Customize your own ChatQnA pipelines. (Optional)
+
+There are two yaml configs you can specify.
+
+* customize.yaml
+This file can specify image names, the number of replicas and CPU cores to mange your pods.
+
+* values.yaml
+This file contains the default microservice configurations for ChatQnA. Please review and understand each parameter before making any changes.
diff --git a/ChatQnA/benchmark/performance/helm_charts/customize.yaml b/ChatQnA/benchmark/performance/helm_charts/customize.yaml
index f5301f06b..718dd2de8 100644
--- a/ChatQnA/benchmark/performance/helm_charts/customize.yaml
+++ b/ChatQnA/benchmark/performance/helm_charts/customize.yaml
@@ -1,7 +1,6 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-
 HUGGINGFACEHUB_API_TOKEN: ${HF_TOKEN}
 
 podSpecs:
@@ -9,7 +8,7 @@ podSpecs:
     spec:
       image_name: opea/chatqna-no-wrapper
       image_tag: latest
-      replicas: 1
+      replicas: 2
       resources:
         limits:
           cpu: "8"
@@ -18,28 +17,6 @@ podSpecs:
           cpu: "8"
           memory: "8000Mi"
 
-  - name: dataprep-deploy
-    spec:
-      image_name: opea/dataprep-redis
-      image_tag: latest
-      replicas: 1
-
-  - name: vector-db
-    spec:
-      image_name: redis/redis-stack
-      image_tag: 7.2.0-v9
-      replicas: 1
-
-  - name: retriever-deploy
-    spec:
-      image_name: opea/retriever-redis
-      image_tag: latest
-      replicas: 2
-      resources:
-        requests:
-          cpu: "4"
-          memory: "4000Mi"
-
   - name: embedding-dependency-deploy
     spec:
       image_name: ghcr.io/huggingface/text-embeddings-inference
@@ -70,3 +47,25 @@ podSpecs:
       resources:
         limits:
           habana.ai/gaudi: 1
+
+  - name: dataprep-deploy
+    spec:
+      image_name: opea/dataprep-redis
+      image_tag: latest
+      replicas: 1
+
+  - name: vector-db
+    spec:
+      image_name: redis/redis-stack
+      image_tag: 7.2.0-v9
+      replicas: 1
+
+  - name: retriever-deploy
+    spec:
+      image_name: opea/retriever-redis
+      image_tag: latest
+      replicas: 2
+      resources:
+        requests:
+          cpu: "4"
+          memory: "4000Mi"