Introduction

Step-Back Prompting is when you ask AI to first think at a higher level before answering your actual question. Instead of jumping directly into solving the problem, you tell it to "step back" and understand the bigger picture first.

This usually makes the final answer smarter, more organized, and more accurate.

Step-Back Prompting = Asking AI to first think about the "kaise karna hai" before jumping into the "kya karna hai."

— hitesh choudhary

Let’s take an example to understand this better

Imagine you're shopping online and you want to buy a laptop. Instead of directly asking:

You first step back and ask:

"What factors should I consider when buying a laptop?"

(Price? Battery life? Processor? RAM? Weight?) Once you have a clear list of factors, THEN you ask:

"Given these factors, now recommend me a laptop."

This is Step-Back Prompting!

You think about how to think first, and then you find the answer. We need to make the AI does the same.

Why do we use Step-Back Prompting

It makes AI more careful and less random.
Forces the AI to think before speaking.
Helps in complex tasks where direct answering can miss important details.
Especially good for RAG systems, ecommerce recommendations, essay writing, reasoning problems, and coding tasks.

Architecture

Code

Before you run your program, make sure to install all the below dependency and create the virtual environment. I am using “uv” to create the virtual environment, for more details checkout the video

https://www.youtube.com/watch?v=8mk85fyzevc

To setup Quadrant DB, use the local setup guide, follow the below resources

https://qdrant.tech/documentation/quickstart/

https://www.youtube.com/watch?v=mHrwS6ZoNKc

Algorithm

Step 1 => Load product documents (e.g., ecommerce product PDF) Step 2 => Split documents into smaller chunks Step 3 => Embed chunks and store in Quadrant VectorDB Step 4 => User asks a query (e.g., "Which is the best gaming phone under 20,000") Step 5 => STEP-BACK PROMPT: First ask AI: "What factors should be considered to find the best gaming phone?" Step 6 => Based on the factors, search the database smartly Step 7 => Retrieve final answers Step 8 => Show user thoughtful, high-quality results

Prompt back implementation

# IMPORTS
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Quadrant
from langchain.schema import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from typing import List
import os
from dotenv import load_dotenv

# LOAD ENVIRONMENT VARIABLES
load_dotenv()

# SET API KEYS
openai_api_key = os.getenv("OPENAI_API_KEY")

# INITIALIZE CHAT MODEL
chat = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# EMBEDDING MODEL
embedding_model = OpenAIEmbeddings(openai_api_key=openai_api_key)

# -------------------------------
# STEP 1: LOAD PDF DOCUMENT
# -------------------------------

def load_documents(pdf_path: str) -> List[Document]:
    loader = PyPDFLoader(pdf_path)
    return loader.load()

# LOAD THE SINGLE ECOMMERCE PDF
pdf_path = "ecommerce_products.pdf"
documents = load_documents(pdf_path)
print(f"Loaded {len(documents)} documents from {pdf_path}")

# -------------------------------
# STEP 2: SPLIT INTO CHUNKS
# -------------------------------

splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(documents)
print(f"Split into {len(chunks)} smaller chunks")

# -------------------------------
# STEP 3: EMBED CHUNKS AND STORE IN VECTOR DB
# -------------------------------

vector_db = Quadrant.from_documents(chunks, embedding=embedding_model)
print("Vectorstore created with embedded chunks.")

# -------------------------------
# STEP 4: STEP-BACK PROMPTING (FIND IMPORTANT FACTORS)
# -------------------------------

def step_back_prompt(query: str) -> str:
    prompt_template = ChatPromptTemplate.from_template(
        """
            You are a smart ecommerce expert. 
            Instead of answering the user's query directly, first think: 
            What are the most important factors to consider to answer this query? 
            Then list them clearly.
            User Query: {query}
        """
    )
    chain = prompt_template | chat
    response = chain.invoke({"query": query})
    return response.content

# USER QUERY
user_query = "best smartphone for gaming under 20000?"

# GET HIGH LEVEL FACTORS
factors = step_back_prompt(user_query)

print("\n=== Step-Back Thought (Important Factors) ===\n")
print(factors)

# -------------------------------
# STEP 5: USE FACTORS TO RETRIEVE BETTER RESULTS
# -------------------------------

# MODIFY SEARCH QUERY
search_query = f"{user_query}. Important factors: {factors}"

# SEARCH VECTOR DB
retrieved_docs = vector_db.similarity_search(search_query, k=5)

print("\n=== Retrieved Documents Based on Step-Back ===\n")
for idx, doc in enumerate(retrieved_docs):
    print(f"{idx+1}. {doc.page_content[:200]}...")

# -------------------------------
# STEP 6: FINAL RESULTS
# -------------------------------

print("\n=== FINAL RECOMMENDATIONS ===\n")
for idx, doc in enumerate(retrieved_docs):
    print(f"{idx+1}. {doc.metadata.get('source', 'Unknown Source')} - {doc.page_content[:150]}...")

Output

Full working code on Github:

https://github.com/gautamkmahato

Conclusion

Step-Back Prompting is a smart way of solving complex questions by first asking the model to step back and find a broader or more general answer, and then using that general answer to solve the specific query. It helps large language models (LLMs) think more clearly, just like how humans sometimes need to zoom out before zooming back in.

Step Back Prompting in RAG: How Thinking Bigger Leads to Smarter AI Answers

Table of contents