diff --git a/README.md b/README.md
index f7dd10d..4a4ad2e 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,10 @@
-# LLMFlowOptimizer (WIP)
+# LLMFlowOptimizer
 
-![Python 3.9](https://img.shields.io/badge/python-3.9-blue.svg)
-![Python 3.10](https://img.shields.io/badge/python-3.10-blue.svg)
+[![python](https://img.shields.io/badge/-Python_3.9_%7C_3.10-blue?logo=python&logoColor=white)](https://github.com/pre-commit/pre-commit)
+[![hydra](https://img.shields.io/badge/Config-Hydra_1.3-89b8cd)](https://hydra.cc/)
+[![Optuna](https://img.shields.io/badge/Optimize-Optuna-blue)](https://github.com/optuna/optuna)
+[![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/)
+[![isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
 
 ## 📌  Introduction
 
@@ -9,11 +12,13 @@ In recent years, various LLMs, embedding models, and LLM flows utilizing them ha
 
 This repository aims to treat LLMs and Embeddings as a hyperparameter, with the goal of automatically searching for the optimal hyperparameter of the LLM flow.
 
-**Below image is the concept image of this repository (image from [Flowise](https://github.com/FlowiseAI/Flowise)). Component of LangChain like a LLM or Embedding can be treated as hyperparameter. You can find optimal component from various candidate.**
-![concept_image](documents/concept.png)
+**Below image is the concept image of this repository (image is took from [Flowise](https://github.com/FlowiseAI/Flowise) and slightly modified). Component of LangChain like a LLM or Embedding can be treated as hyperparameter. You will find component from various candidate that can optimize score.**
+![concept_image](documents/image/concept.png)
 
 This repository is strongly inspired by [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template)🎉
 
+Any feedback, bug reports, and suggestions are appreciated!
+
 ## 🔧  Main Technologies
 
 - [LangChain](https://github.com/langchain-ai/langchain) : LangChain is a framework for developing applications powered by large language models. It can be divided conceptually into components (like llm component, embedding component), which are well-abstracted and easy to switch. This is the reason we can treat each component as hyperparameter.
@@ -26,29 +31,37 @@ This repository is strongly inspired by [lightning-hydra-template](https://githu
 
 - [ragas](https://github.com/explodinggradients/ragas) : Ragas is an evaluation framework for Retrieval Augmented Generation (RAG) pipelines that provides tools based on the latest research for evaluating LLM-generated text to give insights about the RAG pipeline.
 
-# How to use
+# 🚀 How to use
+
+I will explain how to use this repository by using question answering as an example. In example, we use
+
+- **LangChain**  : for model
+- **Ragas** : for evaluation
+- **LangSmith** : for evaluation and monitoring
+
+## Step 0 : Environment Setup
 
 Please click [<kbd>Use this template</kbd>](https://github.com/Yongtae723/LLMFlowOptimizer/generate) to use this repository as template.
 
-Clone your repository and install dependencies by following command.
+- Clone your repository and install dependencies by following command.
 
-```bash
-git clone git@github.com:<YOUR_GITHUB_ID>/<YOUR_REPOSITORY_NAME>.git
-cd <YOUR_REPOSITORY_NAME>
-```
+  ```bash
+  git clone git@github.com:<YOUR_GITHUB_ID>/<YOUR_REPOSITORY_NAME>.git
+  cd <YOUR_REPOSITORY_NAME>
+  ```
 
-## setup
+- We use poetry for package management. you can install poetry and python packages by following command.
 
-We use poetry for package management. you can install poetry and python packages by following command.
+  ```bash
+  pip install poetry
+  poetry install
+  ```
 
-```bash
-pip install poetry
-poetry install
-```
+- Rename `.env.example` to `.env` and set your environment variables to use external API like OpenAI ChatGPT.
 
-## define model architect and config.
+## Step 1 : Define model architect and config.
 
-1. Define model architect like [llmflowoptimizer/modelsample_qa.py](llmflowoptimizer/model/sample_qa.py).
+1. Define model architect like [llmflowoptimizer/component/model/sample_qa.py](llmflowoptimizer/component/model/sample_qa.py).
 
    The arguments in `__init__()` can be used as hyperparameter and will be able to be optimized.
 
@@ -57,38 +70,72 @@ poetry install
    Example model config:
 
    ```yaml
-   defaults: # you can use yaml file on this directly.
-   - _self_
-   - embedding: OpenAI
-   - text_splitter: RecursiveCharacter
-   - llm_for_answer: ChatOpenAI
+    defaults:
+      - _self_
+      - embedding: OpenAI
+      - text_splitter: RecursiveCharacter
+      - llm: OpenAI
+    _target_: llmflowoptimizer.component.model.sample_qa.SampleQA # what we defined on llmflowoptimizer/component/model/sample_qa.py
+    data_path: ${paths.reference_data_dir}/nyc_wikipedia.txt
+   ```
 
-   _target_: llmflowoptimizer.models.sample_qa.SampleQA # what we defined on llmflowoptimizer/models/sample_qa.py
+3. Then you can check your model and config by following command.
 
-   data_path: ${paths.reference_data_dir}/nyc_wikipedia.txt
+   ```bash
+   poetry run python llmflowoptimizer/run.py extras.evaluation=false
    ```
 
-3. then you can check your model and config by following command.
+## Step 2 : Prepare dataset and define evaluation system
 
-```bash
-poetry run python llmflowoptimizer/run.py extras.evaluation=false extras.print_config=true
-```
+- register data on LangSmith (If you want to use LangSmith)
 
-## define evaluation system
+  In our example, we use [LangSmith](https://docs.smith.langchain.com/) for evaluation and monitoring. You can register data on LangSmith by following [example notebook](notebooks/register_data_langsmith.ipynb)
 
-1. Define evaluation system like [llmflowoptimizer/model/evaluation.py](llmflowoptimizer/model/evaluation.py), and set argument on [configs/evaluation](configs/model).
+- Define evaluation system like [llmflowoptimizer/model/evaluation.py](llmflowoptimizer/model/evaluation.py), and set argument on [configs/evaluation](configs/model).
 
-   Optuna will optimize component based on the return value of this evaluation system.
+  Optuna will optimize component based on the return value of this evaluation system.
 
-2. You can check your evaluation system by following command.
+- You can check your evaluation system by following command.
 
-```bash
-poetry run python llmflowoptimizer/run.py
-```
+  ```bash
+  poetry run python llmflowoptimizer/run.py
+  ```
+
+- Since we use Hydra, you can change each LLM flow component by command line argument. For more detail, please check [Override](#override) section.
+
+## Step 3 : Hyperparameter search
+
+- Define search requirement on [configs/hparams_search](configs/hparams_search) like example.
+
+  ```yaml
+  model/text_splitter: choice(RecursiveCharacter, CharacterTextSplitter)
+  model.text_splitter.chunk_size: range(500, 1500, 100)
+  model/llm: choice(OpenAI, GPTTurbo, GPT4)
+  ```
+
+  This example if a part of [configs/hparams_search/optuna.yaml](configs/hparams_search/optuna.yaml), and it means this system will search best hyperparameter from `RecursiveCharacter` or `CharacterTextSplitter` for `model.text_splitter` component, chunk_size is between 500 and 1500, and `OpenAI`, `GPTTurbo`, `GPT4` for `model.llm` component.
+
+  Also complicated search range can be defined by python like [configs/hparams_search/custom-search-space-objective.py](configs/hparams_search/custom-search-space-objective.py)
+
+- You can start hyperparameter search by following command.
+
+  ```bash
+  poetry run python llmflowoptimizer/run.py hparams_search=optuna
+  ```
+
+Then you can see the best parameter on `logs/{task_name}/multirruns/{timestamp}/optimization_results.yaml`.
 
-## dataset
+And you can see the detail of each trial on LangSmith like below.
 
-you can update sample data by following [this notebook](notebooks/register_data_langsmith.ipynb)
+![langsmith_each_trial](documents/image/each_trial.png)
+
+This figure shows the result of each runs.
+
+![langsmith_each_input_output](documents/image/each_input_output.png)
+
+This figure shows the input and output of each questions.
+
+# 📚 Appendix
 
 ## override
 
@@ -100,7 +147,7 @@ When you want to change single parameter, you have to use `.` between parameter
 Example:
 
 ```bash
-poetry run python llmflowoptimizer/run.py model.llm_for_answer.model_name="gpt-4"
+poetry run python llmflowoptimizer/run.py model.text_splitter.chunk_size=1000
 ```
 
 By doing this, you change `model_name` parameter of `llm_for_answer` to gpt-4.
@@ -113,7 +160,7 @@ when you want to change parameter in component scale, you have to define compone
 poetry run python llmflowoptimizer/run.py model/llm_for_answer=OpenAI
 ```
 
-By doing this, LLM flow use [`OpenAI.yaml`](configs/model/llm_for_answer/OpenAI.yaml) model instead of [`ChatOpenAI.yaml`](configs/model/llm_for_answer/ChatOpenAI.yaml) model.
+By doing this, LLM flow use [`OpenAI.yaml`](configs/model/llm_for_answer/OpenAI.yaml) model instead of [`ChatOpenAI.yaml`](configs/model/llm_for_answer/GPT4.yaml) model.
 
 ### Experiment config
 
@@ -125,18 +172,7 @@ after you save config [configs/experiment](configs/experiment), you can override
 poetry run python llmflowoptimizer/run.py experiment=example
 ```
 
-## Hyperparameter search
-
-WIP
-
-## managing experimental results
-
-WIP
-maybe use langflow?
-
-## Appendix
-
-### Test
+## Test
 
 Before you make PR, you have to test your code by following command.
 In sample test, we only check model and evaluation class can be initialized.
@@ -145,7 +181,7 @@ In sample test, we only check model and evaluation class can be initialized.
 make test
 ```
 
-### Auto code formatting
+## Auto code formatting
 
 If you want auto code formatting, you can install by
 
@@ -161,18 +197,36 @@ Also you can format code manually by
 make fix-lint
 ```
 
-# TODO
-
-- \[ \]　.envの説明
-- [ ] Hyperparameter search by optuna
-- [ ] manage experiment config (langsmith? WandB?)
-- [ ] actual experiment result (ask specialist)
-  - [ ] write blog
-- [ ] add test function
-  - [ ] reduce data and execute e2e test
-- [ ] git action
-  - \[ \]lint-check
-  - [ ] test
-- [ ] model can be build from GUI langchain builder
-  - [ ] langflow
-  - [ ] Flowise
+<br>
+<br>
+<br>
+<br>
+
+**DELETE EVERYTHING ABOVE FOR YOUR PROJECT**
+
+______________________________________________________________________
+
+<div align="center">
+
+# Your Project Name
+
+[![python](https://img.shields.io/badge/-Python_3.9_%7C_3.10-blue?logo=python&logoColor=white)](https://github.com/pre-commit/pre-commit)
+[![hydra](https://img.shields.io/badge/Config-Hydra_1.3-89b8cd)](https://hydra.cc/)
+[![Optuna](https://img.shields.io/badge/Optimize-Optuna-blue)](https://github.com/optuna/optuna)
+[![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/)
+[![isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
+<a href="https://github.com/Yongtae723/LLM-Flow-Optimizer"><img alt="Template" src="https://img.shields.io/badge/-LLMFlowOptimizer-blue?style=flat&logo=github&labelColor=gray"></a><br>
+
+</div>
+
+## Description
+
+What it does and what the purpose of the project is.
+
+## Installation
+
+How to install the project.
+
+## How to run
+
+How to run the project.