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This template replicates the "Step-Back" prompting technique that improves performance on complex questions by first asking a "step back" question.

This technique can be combined with regular question-answering applications by doing retrieval on both the original and step-back question.

Read more about this in the paper here and an excellent blog post by Cobus Greyling here

We will modify the prompts slightly to work better with chat models in this template.

Environment Setup​

Set the OPENAI_API_KEY environment variable to access the OpenAI models.


To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package stepback-qa-prompting

If you want to add this to an existing project, you can just run:

langchain app add stepback-qa-prompting

And add the following code to your file:

from stepback_qa_prompting.chain import chain as stepback_qa_prompting_chain

add_routes(app, stepback_qa_prompting_chain, path="/stepback-qa-prompting")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/stepback-qa-prompting")

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