Make Your AI Smarter with Step-Back, Chain of Thought, and HyDE Prompting

Ashwin HegdeAshwin Hegde
7 min read

Table of contents

Overview

AI is becoming a powerful tool for solving complex problems, but what if it could think more like a human? Imagine AI breaking down a problem step by step, thinking about the broader context before diving into the details, or even imagining an answer before searching for relevant documents. That’s where Step-Back Prompting, Chain of Thought Prompting, and HyDE come in—three simple but effective techniques that make AI smarter and help it produce better, more reliable answers.

What is Step-Back Prompting?

Step-Back Prompting encourages AI to take a step back and think about a question in a broader context before jumping into the specifics. Instead of diving straight into the details of a question, AI reframes it into a more general, overarching question. This helps the AI understand the bigger picture before narrowing down to specifics.

Why Use Step-Back Prompting?

  • Clearer answers: By stepping back, AI considers multiple angles before answering.

  • Deeper insights: Reframing questions allows AI to think about general principles that lead to more thoughtful responses.

  • Richer context: It prevents the AI from focusing too narrowly and missing important details.

Example: Reframing a Question with Step-Back Prompting

Let’s say we want to know: What makes Dhoni a successful cricket captain?

Instead of directly answering this, Step-Back Prompting would have the AI first consider: What makes a cricket captain successful?

This broader question helps the AI think about leadership qualities, decision-making skills, and team dynamics before diving into specifics about Dhoni. This results in a more thorough and insightful answer.

Here’s how you can implement Step-Back Prompting using Google’s Gemini Flash 2.0 API in Python:

import os
import google.generativeai as genai

api_key = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=api_key)

model = genai.GenerativeModel('gemini-2.0-flash')

# Original specific question
original_question = "What makes Dhoni a successful cricket captain?"

# Step-back prompt
step_back_prompt = f"""
You are an expert in general knowledge and reasoning. Your task is to take a step back 
and reframe specific questions into broader, more general ones. 
This helps in understanding the bigger picture. Here are some examples:

Q: Why do electric vehicles catch fire during summer in India?
Step-back Q: What factors cause electric vehicles to overheat or catch fire?

Q: How did Kerala handle the 2018 floods?
Step-back Q: How do governments typically respond to large-scale natural disasters?

Now, take a step back and reframe this:
Q: {original_question}
Step-back Q:"""

# Get step-back version
step_back_response = model.generate_content(step_back_prompt)
step_back_question = step_back_response.text.strip()

print("Step-back Question:", step_back_question)

# Now ask Gemini to answer both
final_prompt = f"""
Please provide a detailed answer combining both:
1. General (step-back) question: {step_back_question}
2. Specific question: {original_question}
"""

final_response = model.generate_content(final_prompt)
print("\nAnswer:\n", final_response.text)

In this example, AI reframes the specific question about Dhoni into a broader one and then combines both answers to give a richer, more detailed response.

What is Chain of Thought Prompting?

Chain of Thought Prompting (CoT) encourages AI to think step by step, similar to how we humans break down problems. This method is especially helpful for complex questions or problems that require multiple steps to reach a solution. Instead of simply providing an answer, AI walks through the entire reasoning process, making its logic transparent and trustworthy.

Why Use Chain of Thought Prompting?

  • Clearer reasoning: It helps AI explain its thought process, making the answer more reliable.

  • Improved results for multi-step problems: Complex problems are solved step by step, reducing errors.

  • Enhanced transparency: You can see how the AI arrived at its final answer.

Real-World Example: The Tea Shop in Bengaluru

Let’s apply Chain of Thought to a simple profit calculation problem for a tea shop in Bengaluru.

Problem: A tea shop sells 120 cups of tea in the morning at 10 rupees each, and 80 cups in the evening at 12 rupees each. The shop owner spends 500 rupees on milk and 200 rupees on tea leaves for the whole day. How much profit does the shop owner make in a day?

Here’s how you can use Chain of Thought to solve this step by step:

import os
import google.generativeai as genai

api_key = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=api_key)

model = genai.GenerativeModel('gemini-2.0-flash')

question = """A tea shop in Bengaluru sells 120 cups of tea in the morning at 10 rupees each, 
and 80 cups in the evening at 12 rupees each. If the shop owner spends 500 rupees on milk 
and 200 rupees on tea leaves for the whole day, how much profit does the shop owner make in a day?"""

# Chain of Thought prompt
cot_prompt = f"""
Let's solve the following problem step by step:

Question: {question}

Let's think step by step.
"""

response = model.generate_content(cot_prompt)
print("Chain of Thought Answer:\n", response.text)

In this case, AI would break the problem down into smaller steps like this:

  1. Calculate the total sales:

    • Morning sales = 120 cups × 10 rupees = 1,200 rupees

    • Evening sales = 80 cups × 12 rupees = 960 rupees

    • Total sales = 1,200 + 960 = 2,160 rupees

  2. Calculate the expenses:

    • Milk: 500 rupees

    • Tea leaves: 200 rupees

    • Total expenses = 500 + 200 = 700 rupees

  3. Calculate the profit:

    • Profit = Total sales - Total expenses = 2,160 - 700 = 1,460 rupees

By thinking step by step, the AI provides a clear, logical answer that’s easier to trust.

HyDE: Smarter Search with Hypothetical Answers

When you search for information, you usually type in a question and hope the system finds something similar. But what if your question is unique or worded differently from the documents in your database? That’s where HyDE (Hypothetical Document Embeddings) comes in—a clever way to get better search results using AI.

What is HyDE?

HyDE stands for Hypothetical Document Embeddings. Instead of searching with your question directly, HyDE first asks an AI to imagine what a good answer would look like. Then, it uses this “hypothetical answer” to search for real documents that are similar in meaning.

How Does HyDE Work?

  1. You ask a question.

  2. The AI (like Gemini) generates a hypothetical answer—basically, it makes up a possible answer to your question.

  3. This answer is converted into an embedding (a mathematical fingerprint) using a model like Hugging Face’s all-MiniLM-L6-v2.

  4. The system searches your vector database (like Qdrant) for documents that are close to this embedding.

  5. You get results that are often more relevant and meaningful.

Example: HyDE in Python

Here’s a real-world example using Gemini for the hypothetical answer and Hugging Face for embeddings:

import os
import google.generativeai as genai
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient

# Set up Gemini API
api_key = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=api_key)
llm = genai.GenerativeModel('gemini-2.0-flash')

# Your question
question = "What is FS module?"

# Step 1: Generate a hypothetical answer using Gemini
hypo_prompt = f"Answer this question as if you were an expert: {question}"
hypo_response = llm.generate_content(hypo_prompt)
hypothetical_answer = hypo_response.text.strip()

# Step 2: Get embeddings for the hypothetical answer using Hugging Face
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
hypo_embedding = embeddings.embed_query(hypothetical_answer)

# Step 3: Connect to your existing Qdrant collection
client = QdrantClient(host="localhost", port=6333)
db = QdrantVectorStore(
    client=client,
    collection_name="hyde_demo",
    embedding=embeddings,
)

# Step 4: Search your vector database using the hypothetical answer's embedding
results = db.similarity_search_by_vector(hypo_embedding, k=3)

# Step 5: Show the top results with page numbers
for i, doc in enumerate(results, 1):
    page = doc.metadata.get("page", "N/A")
    print(f"\nResult {i} (Page {page}):\n{doc.page_content}")

Conclusion

These three techniques — Step-Back Prompting, Chain of Thought Prompting, and HyDE — can dramatically improve the way AI works. Whether it's stepping back for a broader view, thinking step-by-step, or imagining hypothetical answers for smarter search, these tools help AI generate responses that are clearer, more accurate, and more human-like. Give them a try in your next AI project, and watch how much smarter your AI becomes!

Source Code

You can find the complete code implementation in this GitHub repository:
👉 Ashwinhegde19

Let's Connect:

Feel free to connect with me on LinkedIn or follow me on Twitter for more updates on tech, AI, and development:

0
Subscribe to my newsletter

Read articles from Ashwin Hegde directly inside your inbox. Subscribe to the newsletter, and don't miss out.

ChaiCodelangchaingeminigenaiModel

Written by

Ashwin Hegde
Ashwin Hegde