Prompt chaining: breaking complex tasks down into steps

When using Large Language Models (LLMs) for advanced workflows, you quickly notice that monolithic prompts reach their limits. If you ask a single prompt to generate code, write unit tests, draft documentation, and check for security vulnerabilities, the likelihood of hallucinations, forgotten instructions, and suboptimal quality increases drastically. The solution to this is prompt chaining: breaking down a complex goal into sequential, manageable subtasks.

Why prompt chaining?

Prompt chaining brings structure and predictability to AI applications. Instead of relying on 'hope' that the model will produce the perfect end result in one go, you enforce modularity:

• Limited context window load: Each model in the chain receives exactly the context and instructions needed for that specific step, without noise from redundant side issues.
• Targeted optimization: If step 2 fails, you don't have to adjust the entire system. You only fine-tune the prompt for that specific step.
• Higher reliability: Smaller tasks have a significantly lower error rate with modern LLMs than giant multi-task prompts.

Linking output to input

The heart of prompt chaining is the data flow. The output of Step N serves as controlled input for Step N+1. This can be done manually in a chat environment, but truly shines in programmatic pipelines (such as Python scripts or orchestration tools).

To ensure that the model in the next step can immediately work with the provided data, it is crucial to work with structured formats such as JSON or clear Markdown sections between the steps.

Error handling between steps

In a chain, things can always go wrong: a model returns incomplete code, forgets a JSON field, or produces invalid syntax. Robust prompt chains therefore build in validation checks:

• Validation steps (Validators): Add a deterministic check (or a fast, cheap LLM check) between crucial steps to verify that the output conforms to predefined schemas.
• Fallback mechanisms: If validation fails, you send the output along with the specific error message back to the prompt to automatically perform a correction before the chain proceeds to the next phase.

Concrete example: From raw note to technical backlog

Suppose you want to convert messy meeting notes into ready-to-use user stories for your development team, including technical acceptance criteria. You can break this down into a chain of three steps:

Step 1: Core feature extraction

Goal: Filter raw text and isolate all explicit and implicit functionalities.

System: You are an accurate analyst. Filter all functional requirements from the minutes below.
Input: "[Raw meeting notes about a new user login...]"
Output: A structured list of raw feature requirements.

Step 2: Generating User Stories (Gherkin syntax)

Goal: Convert the filtered requirements into professional user stories with acceptance criteria.

System: Receive the list of feature requirements from the previous step. For each requirement, write a user story in the format 'As a [role], I want [functionality], so that [value]', including Given-When-Then acceptance criteria.
Input: [Output of Step 1]
Output: Markdown document with formal user stories.

Step 3: Technical risk and security check

Goal: Check the generated stories for potential security vulnerabilities (such as OWASP Top 10) or technical bottlenecks.

System: Critically check the user stories below for security risks, missing validations, and performance bottlenecks. Add security tasks where necessary.
Input: [Output of Step 2]
Output: Final, approved backlog items with security notes.