diff --git a/examples/job_info_extractor/README.md b/examples/job_info_extractor/README.md
index 757455e2ca..4b0b51cc61 100644
--- a/examples/job_info_extractor/README.md
+++ b/examples/job_info_extractor/README.md
@@ -1,9 +1,49 @@
-# Using this template
+# Extraction using OpenAI Functions and Langchain"
 
-Please make sure to create a `.env` file with your OpenAI API key before running the app.
-OPENAI_API_KEY=sk-xxxxxxx
 
-You can find your keys here:
-https://platform.openai.com/account/api-keys
+This templates is designed to extracts job information (company name, job 
+title, salary range) from a job description. It uses OpenAI Functions and 
+Langchain. It runs with agenta. 
+[Agenta](https://github.com/agenta-ai/agenta) is an open-source LLMOps 
+platform that allows you to 1) quickly experiment and compare 
+configuration for LLM apps 2) evaluate prompts and workflows 3) deploy 
+applications easily. 
+
+## How to use
+### 0. Prerequisites
+-  Install the agenta CLI
+```bash
+pip install agenta-cli
+```
+- Either create an account in [agenta cloud](https://cloud.agenta.ai/) or 
+[self-host agenta](/self-host/host-locally)
+
+### 1. Clone the repository
+
+```bash
+git clone https://github.com/Agenta-AI/job_extractor_template
+```
+
+### 2. Initialize the project
+
+```bash
+agenta init
+```
+
+### 3. Setup your openAI API key
+Create a .env file by copying the .env.example file and add your openAI 
+API key to it.
+```bash
+OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx
+```
+
+### 4. Deploy the application to agenta
+
+```bash
+agenta variant serve app.py
+```
+
+### 5. Experiment with the prompts in a playground and evaluate different variants in agenta
+
+https://github.com/Agenta-AI/job_extractor_template/assets/4510758/30271188-8d46-4d02-8207-ddb60ad0e284
 
-Go back to the [Getting started tutorial](https://docs.agenta.ai/getting-started) to continue
\ No newline at end of file
diff --git a/examples/job_info_extractor/app.py b/examples/job_info_extractor/app.py
index 4ccd38a583..8d4dd21941 100644
--- a/examples/job_info_extractor/app.py
+++ b/examples/job_info_extractor/app.py
@@ -11,11 +11,17 @@
 
 from pydantic import BaseModel, Field
 
-default_prompt = "What is a good name for a company that makes {product}?"
+CHAT_LLM_GPT = [
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo",
+    "gpt-4",
+]
 
 ag.init()
 ag.config.default(
-    prompt_template=ag.TextParam(default_prompt),
     system_message=ag.TextParam(
         "You are a world class algorithm for extracting information in structured formats."
     ),
@@ -26,10 +32,11 @@
     company_desc_message=ag.TextParam("The name of the company"),
     position_desc_message=ag.TextParam("The name of the position"),
     salary_range_desc_message=ag.TextParam("The salary range of the position"),
-    temperature=ag.FloatParam(0.5),
-    top_p=ag.FloatParam(1.0),
+    temperature=ag.FloatParam(0.9),
+    top_p=ag.FloatParam(0.9),
     presence_penalty=ag.FloatParam(0.0),
     frequency_penalty=ag.FloatParam(0.0),
+    model=ag.MultipleChoiceParam("gpt-3.5-turbo-0613", CHAT_LLM_GPT),
 )
 
 
@@ -50,7 +57,7 @@ def generate(
 ) -> str:
     """Extract information from a job description"""
     llm = ChatOpenAI(
-        model="gpt-3.5-turbo-0613",
+        model=ag.config.model,
         temperature=ag.config.temperature,
         top_p=ag.config.top_p,
         presence_penalty=ag.config.presence_penalty,