Prompt Chaining in Python — ELI5
Imagine you want to plan a birthday party. You do not figure out the cake, the guest list, and the decorations all at once. First you pick a theme. Then you choose food that matches the theme. Then you write invitations that match the food and theme. Each step uses the answer from the step before.
Prompt chaining works the same way with AI. Instead of writing one giant instruction that tries to do everything, you break the job into small steps. Step one might say “summarize this article.” Step two takes that summary and says “pull out the three main facts.” Step three takes those facts and says “write a social-media post.”
Why bother? Because when you ask an AI to do ten things at once, it often mixes them up or forgets half. Small, focused prompts give much better results.
People sometimes think chaining means the AI “remembers” everything on its own. It does not. Your code is the glue that passes each answer forward to the next prompt. The AI only sees what you hand it.
A good first chain to try: take user input, clean it up in prompt one, answer the cleaned question in prompt two, and format the answer nicely in prompt three. Three simple steps, much better output.
The one thing to remember: Prompt chaining splits a big AI task into small, reliable steps where each prompt builds on the last one’s output — and your Python code is the glue holding the relay together.
See Also
- Python Agent Frameworks An agent framework gives AI the ability to plan, use tools, and work through problems step by step — like upgrading a calculator into a research assistant.
- Python Embedding Pipelines An embedding pipeline turns words into numbers that capture meaning — like translating every sentence into coordinates on a giant map of ideas.
- Python Guardrails Ai Guardrails are safety bumpers for AI — they check what the model says before it reaches users, like a spellchecker but for facts, tone, and dangerous content.
- Python Llm Evaluation Harness An LLM evaluation harness is like a report card for AI — it runs tests and grades how well the model answers questions so you know if it is actually improving.
- Python Llm Function Calling Function calling lets an AI ask your Python code for help — like a chef who can read a recipe but needs someone else to actually open the fridge.