# stepback-qa-prompting 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](https://arxiv.org/abs/2310.06117) and an excellent blog post by Cobus Greyling [here](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb) 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. ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package stepback-qa-prompting ``` If you want to add this to an existing project, you can just run: ```shell langchain app add stepback-qa-prompting ``` And add the following code to your `server.py` file: ```python 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](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/stepback-qa-prompting/playground](http://127.0.0.1:8000/stepback-qa-prompting/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/stepback-qa-prompting") ```