> For the complete documentation index, see [llms.txt](https://karini-ai.gitbook.io/karini-ai-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://karini-ai.gitbook.io/karini-ai-documentation/prompt-optimization-experiments/set-up-and-execute-experiment.md).
# Set up and execute experiment
To start an experiment, navigate to the **Prompt Optimization Experiments** section and click the **Add new** button.
Follow these steps to set up and execute a **Prompt Optimization Experiment**:
### **Step 1: Define the Experiment**
* Enter a **descriptive name** for your experiment in the Experiment Name field.
* Provide **a clear and concise description** of the experiment’s objective in the Prompt Description field. This will guide the optimization process.
### Step 2: Configure the Initial Prompt
* Select an existing prompt from the Select Prompt dropdown.
* Upload a CSV file that includes a field for each prompt input variable along with its corresponding ground truth answer. This dataset will be utilized to evaluate your prompt responses and optimize the prompt.
* Click **Show Dataset** to preview the uploaded dataset to verify its formatting and ensure it aligns with the required structure before proceeding with prompt optimization.
### Step 3: Specify any necessary improvements
* It allows you to specify enhancements required for the prompt.
* You can select one or more improvements from various predefined improvement suggestions to refine their prompt.
* The available options include:
* Refine for Clarity
* Shorten for Conciseness
* Add Specific Examples
* Rephrase for Tone Consistency
* Improve Structure
* Make the Prompt More Verbose
* Make the Prompt More Concise
* You can customize and add specific improvements based on your requirements.
* You can delete any added improvements using the delete button.
### Step 4: Set Up the Evaluation Parameters
* Choose **a Judge LLM** by selecting an appropriate model from the Model dropdown in the **Judge LLM** section.
* Set maximum number of prompt optimization iterations to be performed for each candidate LLM.
### Step 5: Add Candidate LLMs for Evaluation
* Add the LLM endpoint(s) that will be tested for performance evaluation.
### Step 6: Save the Experiment
* Click "**Save**" to store the experiment setup for future reference or modifications.
* The system allows you to save prompt optimization experiments at any stage, ensuring flexibility in the setup process.
### Step 7: Execute the Experiment
* Once all configurations are complete, click "Run Optimization" to start the prompt refinement process.
* Upon selecting "Run Optimization," a confirmation pop-up will be displayed to verify the initiation of the optimization process.
### Cloning a Prompt Optimization Experiment
**Karini AI** supports cloning a prompt optimization experiment. The clone functionality enables you to replicate an **existing prompt optimization experiment**, preserving all associated configurations, including **the initial prompt, evaluation dataset, requested improvements, Judge LLM ,candidate LLM, model parameters**, and iteration settings. This feature facilitates **iterative experimentation**, allowing you to adjust specific parameters and explore variations without modifying the original experiment.
**How to Clone an Experiment**
To start the cloning process, follow these steps:
1. **Navigate to the experiment Dashboard** -Locate the experiment you want to clone.
2. **Click the "Clone" Button**:-Found on the experiment details page.
3. **Edit the New Experiment (Optional) -**Once cloned, the new experiment retains all settings but can be modified independently.
4. Enter a name for the experiment.
5. **Run the Experiment** – Execute the cloned experiment with updated configurations as needed.
