Ah, prompt chaining—the digital equivalent of playing “telephone” with AI, except unlike your childhood games, the message actually improves as it travels! This foundational concept in the world of large language models (LLMs) involves connecting multiple prompts in a sequence where one prompt’s output becomes the next prompt’s input. It’s like training your AI to play a game of Connect Four, but with words instead of plastic discs.
Understanding Prompt Chaining
At its heart, prompt chaining is about creating an AI assembly line. Instead of having one prompt do all the heavy lifting (and potentially collapse under pressure like me at the gym), you divide the work into manageable chunks. Think of it as the difference between asking someone to build an entire house versus asking them to lay the foundation, then the walls, then the roof—except in this case, that “someone” is an AI that doesn’t complain about back pain.
Types of Prompt Chaining
Sequential Chaining: The AI equivalent of a conga line. One prompt follows directly after another in a straight line. Perfect for when your workflow is as straightforward as your uncle’s political opinions at Thanksgiving dinner.
How it works: Each prompt’s output becomes the complete input for the next prompt, creating a linear pipeline of processing steps.
Example: You feed a news article into an AI that first extracts key facts, then summarizes those facts, and finally generates social media posts based on that summary. Like assembly-line workers each handling one specific task, each prompt has a specialized job that builds on the previous one:
Article → [Extract Facts] → Key Facts → [Summarize] → Summary → [Create Posts] → Social PostsBranching Chaining: One output splits into multiple paths, like a choose-your-own-adventure book for AIs. “If you want to summarize the text, turn to page 3. If you want to analyze sentiment, turn to page 5.”
How it works: The same output is processed through different prompt chains simultaneously, allowing multiple operations to be performed on the same input without redundancy.
Example: A customer review gets processed into three parallel workflows – one analyzing sentiment, another extracting product features mentioned, and a third identifying suggestions for improvement. It’s like giving the same document to three different departments for specialized analysis:
Review → [Branch]
├─→ [Sentiment Analysis] → Positive/Negative Score
├─→ [Feature Extraction] → List of Product Features
└─→ [Suggestion Extraction] → Improvement Ideas
Iterative Chaining: The AI keeps repeating steps until it gets things right—essentially the computational version of your partner saying “I’ll stop asking when you give the right answer.”
How it works: The output is repeatedly fed back into the same prompt (or chain) multiple times, with each iteration refining the previous output until a certain condition is met.
Example: An AI drafting an email keeps improving its own work through multiple revisions. Each draft gets fed back into a “refine this draft” prompt until the quality score exceeds a threshold or no significant improvements are detected. Like a student who keeps erasing and rewriting until they’re satisfied:
Initial Draft → [Improve] → Draft v2 → [Improve] → Draft v3 → [Improve until quality > 8.5] → Final DraftHierarchical Chaining: Breaking big tasks into smaller ones, like how you approach eating an elephant—one bite at a time (not that I recommend eating elephants, they’re endangered and probably tough).
How it works: Complex tasks are decomposed into subtasks organized in a tree-like structure, with higher-level prompts coordinating the results from lower-level prompts.
Example: Generating a business plan where high-level sections (“Executive Summary”) receive input from multiple lower-level analyses (“Market Analysis,” “Financial Projections”). The executive summary doesn’t get written until all the component analyses are complete, similar to how a manager needs reports from all departments before making a final decision:
[Generate Business Plan]
↑
┌───────────────────────┼───────────────────────┐
↓ ↓ ↓
[Market Analysis] [Financial Projections] [Operations Plan]
↑ ↑ ↑
┌─────┴─────┐ ┌────┴────┐ ┌────┴────┐
↓ ↓ ↓ ↓ ↓ ↓
[Competitor] [Customer] [Revenue] [Costs] [Staffing] [Logistics]
[Analysis] [Needs] [Forecast] [Analysis] [Plan] [Plan]
Conditional Chaining: The AI makes decisions based on previous outputs. It’s like a digital version of “If this, then that,” but without the existential crisis that typically follows philosophical statements.
How it works: The workflow dynamically selects different prompt paths based on the content or classification of previous outputs, enabling decision-making.
Example: A customer service AI analyzes a message, determines its category (complaint, question, or praise), and then routes it to specialized handling prompts. Like a receptionist who determines which department should handle your call:
Message → [Classify Type]
├─→ If "Complaint" → [Escalation Assessment] → [Generate Apology/Solution]
├─→ If "Question" → [Knowledge Base Search] → [Formulate Answer]
└─→ If "Praise" → [Thanks Generator] → [Upsell Opportunity Assessment]Multimodal Chaining: Handling different data types in one workflow. Text, images, audio—it’s the AI equivalent of being a renaissance person, but without the fancy hat.
How it works: Specialized prompts process different types of data (text, images, audio) and their outputs are combined or transferred between modalities in a cohesive workflow.
Example: A product review system that processes both text reviews and product images, combining the textual sentiment analysis with image-based feature detection to create a comprehensive report. It’s like having a literary critic and an art critic collaborate on a single review:
Text Review → [Text Analysis] → Sentiment Score
↓
Product Image → [Image Analysis] → Feature Detection
↓
[Combined Analysis] → Comprehensive ReportDynamic Chaining: Adapts on the fly based on changing conditions. If prompt chaining were a dance, this would be improvisation rather than following choreography.
How it works: The structure of the chain itself is modified during execution based on previous results, external inputs, or changing requirements.
Example: A research assistant AI that builds its prompt chain as it explores a topic. If initial research reveals unexpected subtopics, it dynamically adds new research branches for those topics. Like a detective who keeps adjusting their investigation based on new clues:
Initial Research → [Topic Assessment]
├─→ If "Needs Historical Context" → [Add Historical Research Branch]
├─→ If "Contains Technical Terms" → [Add Definition Lookup Branch]
└─→ If "Has Conflicting Sources" → [Add Source Comparison Branch]
Recursive Chaining: Divides and conquers large inputs before combining results. Like how you might tackle cleaning your house room by room instead of staring at the whole mess and ordering takeout.
How it works: Large inputs are split into manageable chunks, processed individually through the same chains, and the results are then aggregated.
Example: Summarizing a 300-page book by processing each chapter through the same summary chain, then combining those summaries into a final book summary. It’s like multiple interns each reading different chapters and then collaborating on the final report:
Book → [Split into Chapters]
├─→ Chapter 1 → [Summarize] → Chapter 1 Summary
├─→ Chapter 2 → [Summarize] → Chapter 2 Summary
├─→ ...
└─→ Chapter N → [Summarize] → Chapter N Summary
All Summaries → [Aggregate] → Complete Book Summary
Reverse Chaining: Starts with the desired output and works backward. It’s the computational version of “I know where I want to end up, now how do I get there?” – essentially the opposite of most road trips I’ve taken.
How it works: Beginning with the end goal, the system determines what inputs or intermediate steps would be needed to achieve that output.
Example: A curriculum design system that starts with learning objectives and works backward to determine what lessons, activities, and assessments would lead to those outcomes. Like a chef who decides on the final dish and then works out what ingredients to buy and steps to follow:
Desired Learning Outcomes
↓
[Identify Necessary Skills]
↓
[Determine Required Knowledge]
↓
[Design Assessment Methods]
↓
[Create Learning Activities]
↓
Complete Curriculum PlanImplementing Prompt Chaining with LangChain
LangChain is the Swiss Army knife for prompt chaining, offering tools to manage LLMs and connect prompts into workflows. It handles the complicated bits so you can focus on solving problems rather than getting lost in a tangled web of prompt management. It’s like having a really organized friend who keeps your messy ideas in check.
Example: Multistep Text Processing
Let’s say you want to process customer feedback—not because you enjoy reading complaints (who does?), but because you need insights. Here’s how prompt chaining makes this less painful:
Extracting Keywords (Sequential Chaining)
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain
# Define the prompt template
keyword_template = PromptTemplate.from_template(
"Extract the keywords from the following text:\n\n{text}"
)
# Initialize the LLM
llm = OpenAI()
# Create the chain
keyword_chain = LLMChain(prompt=keyword_template, llm=llm)
# Input text
text = "I love the new design of your app, but it crashes frequently."
# Run the chain
keywords = keyword_chain.run({"text": text})
print(keywords)Output: “new design, app, crashes”
The AI just performed digital highlighting without wasting a single marker.
Summarizing Content (Sequential Chaining)
# Define the prompt template
summary_template = PromptTemplate.from_template(
"Summarize the following text:\n\n{text}"
)
# Create the chain
summary_chain = LLMChain(prompt=summary_template, llm=llm)
# Run the chain
summary = summary_chain.run({"text": text})
print(summary)Output: “User appreciates the new design but experiences frequent crashes.”
Congratulations! The AI just read between the lines faster than your English teacher ever could.
Classifying Sentiment (Sequential Chaining)
# Define the prompt template
sentiment_template = PromptTemplate.from_template(
"Classify the sentiment of the following text as positive, neutral, or negative:\n\n{text}"
)
# Create the chain
sentiment_chain = LLMChain(prompt=sentiment_template, llm=llm)
# Run the chain
sentiment = sentiment_chain.run({"text": text})
print(sentiment)Output: “Mixed sentiment”
The AI has now officially become better at detecting passive-aggressive comments than most humans.
By chaining these prompts together, we’ve created an automated system that extracts keywords, summarizes content, and classifies sentiment—all without having to bribe an intern with coffee.
When to Use Prompt Chaining
Use prompt chaining when your task is more complex than a one-prompt wonder can handle. It’s perfect for situations where you need the AI equivalent of a relay team rather than a solo marathon runner. Customer support chatbots, content analysis, research assistants—they all benefit from breaking tasks into manageable chunks.
Think of it this way: you wouldn’t ask a single person to simultaneously cook dinner, wash the car, and file your taxes (unless you’re a terrible boss). So why ask a single prompt to do everything?
Conclusion
Prompt chaining is like LEGO for AI workflows—connecting simple blocks to build something amazing. With frameworks like LangChain, you don’t need a PhD in computer science to create sophisticated AI systems. Just connect the right prompts in the right order, and watch your AI perform tasks with the precision of a Swiss watch and the complexity of a Rube Goldberg machine—but hopefully with better results than the latter.
Interested in hands-on AI education to really get good at this? Training is kinda my thing…