101-Prompt Chains
Last updated
Last updated
Prompt chains are sequences of interconnected prompts designed to guide Large Language Models (LLMs) through complex, multi-step tasks. By breaking down intricate processes into smaller, manageable steps, prompt chains enable LLMs to tackle more sophisticated problems, maintain context across multiple interactions, and produce more accurate and relevant outputs. This approach leverages the strengths of LLMs while mitigating their limitations in handling extensive context or complex reasoning in a single prompt.
Sequential Processing: Breaking down complex tasks into a series of simpler steps.
Context Preservation: Maintaining relevant information across multiple prompts.
Intermediate Outputs: Using the output of one prompt as input for the next.
Conditional Branching: Adapting the chain based on intermediate results or user input.
This prompt chain breaks down a complex text analysis task into four distinct steps, allowing the LLM to focus on each aspect separately while building upon previous outputs.
This chain guides the LLM through the process of creating content, from outlining to drafting, reviewing, and refining, resulting in a higher quality final product.
Start with a clear overall objective for the prompt chain.
Break down complex tasks into logical, manageable steps.
Ensure each step in the chain builds upon or utilizes the output from previous steps.
Include error checking or validation steps where appropriate.
Allow for user input or review between steps when necessary.
Loss of Context: Ensure important context is carried forward in each step of the chain.
Overly Complex Chains: Keep chains as simple as possible while still achieving the desired outcome. Overly long chains can lead to accumulated errors.
Lack of Flexibility: Design chains that can adapt to unexpected outputs or user inputs. Consider including conditional steps or branches.
GenAI University: 101-Context Window
GenAI University: 101-Prompt Engineering
Tailwinds Feature: Prompt Template
Tailwinds Feature: Variables
Multi-step Analysis
Analyzing a text document for sentiment, key topics, and actionable insights.
Provides a comprehensive analysis by breaking down the task into manageable steps.
Iterative Content Creation
Generating an outline, drafting content, and then refining based on specific criteria.
Produces higher quality content through a structured, iterative process.
Complex Problem Solving
Solving math or logic problems by breaking them down into smaller steps.
Enables LLMs to tackle more complex problems by following a step-by-step approach.