AI

Build a Prompt Chain Pipeline

Easy
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Prompt Chaining

Prompt chaining connects multiple LLM calls where the output of one becomes the input to the next.

Task

Implement a PromptChain class that:

  1. Allows adding callable steps (functions or lambdas).
  2. Executes steps sequentially, passing output to the next step.
  3. Returns the final output.
  4. Captures intermediate outputs for debugging.

Constraints

  • Steps must be callable.
  • Empty chain should return the input unchanged.
  • Each step's output must be a string.

Examples

Example 1:
Input: chain = PromptChain() chain.add_step(lambda x: x.upper()) chain.add_step(lambda x: f'Result: {x}') chain.run('hello')
Output: 'Result: HELLO'
Explanation: Step 1 uppercases, step 2 prepends 'Result: '.

Starter Code

from typing import List, Callable

class PromptChain:
    def __init__(self):
        self.steps: List[Callable[[str], str]] = []

    def add_step(self, fn: Callable[[str], str]) -> 'PromptChain':
        # TODO: Add a transformation step
        pass

    def run(self, initial_input: str) -> str:
        # TODO: Execute chain sequentially
        pass
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