Prompt engineering quickstart (SDK)

This quick start will walk through how to create, test, and iterate on prompts using the SDK. In this tutorial we will use OpenAI, but you can use whichever LLM you want.

This tutorial uses the SDK for prompt engineering, if you are interested in using the UI instead, read this guide.

1. Setup

First, install the required packages:

Python

pip install -qU langsmith openai langchain_core

Next, make sure you have signed up for a LangSmith account, then create and set your API key. You will also want to sign up for an OpenAI API key to run the code in this tutorial.

LANGSMITH_API_KEY = '<your_api_key>'OPENAI_API_KEY = '<your_api_key>'

2. Create a prompt

To create a prompt in LangSmith, define the list of messages you want in your prompt and then wrap them using the ChatPromptTemplate function (Python) or TypeScript function. Then all you have to do is call push_prompt (Python) or pushPrompt (TypeScript) to send your prompt to LangSmith!

Python

from langsmith import Client
from langchain_core.prompts import ChatPromptTemplate

# Connect to the LangSmith client
client = Client()

# Define the prompt
prompt = ChatPromptTemplate([
    ("system", "You are a helpful chatbot."),
    ("user", "{question}"),
])

# Push the prompt
client.push_prompt("my-prompt", object=prompt)

3. Test a prompt

To test a prompt, you need to pull the prompt, invoke it with the input values you want to test and then call the model with those input values. your LLM or application expects.

Python

from langsmith import Client
from openai import OpenAI
from langchain_core.messages import convert_to_openai_messages

# Connect to LangSmith and OpenAI
client = Client()
oai_client = OpenAI()

# Pull the prompt to use
# You can also specify a specific commit by passing the commit hash "my-prompt:<commit-hash>"
prompt = client.pull_prompt("my-prompt")

# Since our prompt only has one variable we could also pass in the value directly
# The code below is equivalent to formatted_prompt = prompt.invoke("What is the color of the sky?")
formatted_prompt = prompt.invoke({"question": "What is the color of the sky?"})

# Test the prompt
response = oai_client.chat.completions.create(
    model="gpt-4o",
    messages=convert_to_openai_messages(formatted_prompt.messages),
)

4. Iterate on a prompt

LangSmith makes it easy to iterate on prompts with your entire team. Members of your workspace can select a prompt to iterate on, and once they are happy with their changes, they can simply save it as a new commit.

To improve your prompts:

• We recommend referencing the documentation provided by your model provider for best practices in prompt creation, such as Best practices for prompt engineering with the OpenAI API and Gemini’s Introduction to prompt design.

• To help with iterating on your prompts in LangSmith, we’ve created Prompt Canvas — an interactive tool to build and optimize your prompts. Learn about how to use Prompt Canvas.

To add a new commit to a prompt, you can use the same push_prompt (Python) or pushPrompt (TypeScript) methods as when you first created the prompt.

Python

from langsmith import Client
from langchain_core.prompts import ChatPromptTemplate

# Connect to the LangSmith client
client = Client()

# Define the prompt to update
new_prompt = ChatPromptTemplate([
    ("system", "You are a helpful chatbot. Respond in Spanish."),
    ("user", "{question}"),
])

# Push the updated prompt making sure to use the correct prompt name
# Tags can help you remember specific versions in your commit history
client.push_prompt("my-prompt", object=new_prompt, tags=["Spanish"])

5. Next steps

• Learn more about how to store and manage prompts using the Prompt Hub in these how-to guides
• Learn more about how to use the playground for prompt engineering in these how-to guides

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