Prompt Chaining

Prompt Chaining is the practice of decomposing a complex task into a series of smaller, sequential sub-tasks. Each sub-task is handled by a specific LLM call, with the output of one step feeding into the next. This approach improves reliability, testability, and allows for intermediate processing (like validation or formatting) between steps.

When to Use

• Complex Transformations: When a single prompt is too complex or prone to error (e.g., “Research topic X, then write an article, then translate it”).
• Step-by-Step Logic: When the logic requires a strict sequence of operations (e.g., Extract Data -> Validate Data -> Summarize Data).
• Token Limits: When the input or intermediate context exceeds the context window of a single call.
• Debugging: To isolate failures in a complex workflow by inspecting intermediate outputs.

Use Cases

• Document Processing: Extract text -> Summarize -> Translate -> Format as JSON.
• Code Generation: Write tests -> Write code to pass tests -> Refactor code.
• Content Creation: Generate outline -> Draft sections -> Polish tone -> Generate Title.

Implementation Pattern

def prompt_chain_workflow(input_data):
    # Step 1: Extraction
    # Focuses solely on getting the right data out of the raw input.
    extracted_data = llm_call(
        prompt="Extract key entities from this text...",
        input=input_data
    )
    
    # Optional: Deterministic Validation
    # We can run code check here before proceeding.
    if not validate(extracted_data):
        raise ValueError("Extraction failed")

    # Step 2: Transformation
    # Focuses on converting the data into the desired format/style.
    final_output = llm_call(
        prompt="Transform this extraction into a marketing summary...",
        input=extracted_data
    )
    
    return final_output