Prompt Chaining

Description

Prompt chaining refers to the technique of not confronting a language model (LLM) with a single, complex instruction, but rather dividing the work process into several consecutive steps. Although LLMs are very good at following individual instructions, accuracy often decreases when too many instructions or complex tasks are combined in the prompt. To avoid this problem, instructions are broken down into logical subtasks and processed one after the other. Each step builds on the result of the previous one, creating a chain of prompts and responses.

Prompt chaining allows the model to work more systematically, reduce sources of error, and solve even complex tasks reliably and comprehensibly. The intermediate results of each subtask are explicitly recorded and specifically processed in the next step.

Advantages

• Complex tasks become manageable by breaking them down into simple, sequential steps.
• The results of each sub-step can be manually checked or adjusted, which increases control.
• There is less risk of misunderstandings and errors on the part of the model, as the tasks are clearly structured and separated.

Typical examples

Generating content (e.g., blog articles)

Prompt 1: “Brainstorm 5 blog titles about AI in healthcare.”
Prompt 2: (User selects a title) “Draft an outline for a blog post titled ‘[selected title]’ and divide it into 3 main sections.”
Prompt 3: “Write the introduction for the provided outline.”
Prompt 4: “Expand section 1 from the outline...” etc.

Data analysis/evaluation

Prompt 1: “Extract the most important entities from this document.”
Prompt 2: “Summarize the relationship between these entities.”
Prompt 3: “Generate a report based on the summary.”

Areas of application

Prompt chaining is particularly suitable when tasks are too extensive or too complex to be reliably processed in a single prompt. The approach is ideal for processing raw data, automated content pipelines, analysis workflows, and any type of step-by-step process that requires a high degree of accuracy or traceability.