Intermediate6 prompts

Prompt Chaining

Prompt chaining decomposes a complex task into a sequence of smaller prompts, where the output of each step feeds into the next. Each link in the chain has a clear, focused responsibility, improving accuracy and giving you control at every stage.

How It Works

1

Decompose the goal into 3-7 discrete sub-tasks.

2

Define the output contract of each step (format, length).

3

Feed step 1 output into step 2 as context.

4

Review and adjust at each checkpoint before continuing.

Real Example

Content pipeline: 1) Brainstorm 10 topic angles. 2) Pick the strongest and create an outline. 3) Draft each section with the outline as context. 4) Edit for tone and clarity. 5) Generate title, meta description, and tags.

Best Practices

  • Keep each link single-purpose.
  • Define exactly what data passes between steps.
  • Add checkpoints to review intermediate outputs.
  • Standardize step outputs as reusable templates.

Common Mistakes

  • Chaining without a clear data contract between steps.
  • Too many steps for a simple task.
  • Losing context because earlier outputs are not preserved.

When to Use Chaining

Long-form contentComplex data pipelinesQuality-critical multi-stage work

Frequently Asked Questions

When should I use Prompt Chaining?

Long-form content, Complex data pipelines, Quality-critical multi-stage work. If your task is more complex or your output is inconsistent, consider combining it with examples or chain-of-thought.

Does Prompt Chaining work on all AI models?

Most prompting techniques transfer across modern models like ChatGPT, Claude, and Gemini, though results vary. Test the same prompt on two models and keep the best performer.

What is the next technique to learn after Chaining?

Based on your related techniques, explore: Prompt Basics, Tree of Thoughts, Agent Prompting.

How do I practice Chaining?

Use the prompts above as starting points, then modify one variable at a time. Track which changes improve your results in a prompt library.

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