Sequential Workflow

Coordinating a chain of LLM calls to solve a complex task.

A workflow where the output of one LLM call becomes the input for the next. This sequential design allows for structured reasoning and step-by-step task completion.

Workflow Architecture

Chain multiple LLM calls sequentially to process complex tasks.

Sequential Workflow Cookbook

For a more detailed walk-through refer to the notebook here

Setup Client

Python

from together import Together

client = Together();

def run_llm(user_prompt : str, model : str, system_prompt : str = None):
    messages = []
    if system_prompt:
        messages.append({"role": "system", "content": system_prompt})

    messages.append({"role": "user", "content": user_prompt})

    response = client.chat.completions.create(
        model=model,
        messages=messages,
        temperature=0.7,
        max_tokens=4000,
    )

    return response.choices[0].message.content

Implement Workflow

Python

from typing import List

def serial_chain_workflow(input_query: str, prompt_chain : List[str]) -> List[str]:
    """Run a serial chain of LLM calls to address the `input_query`
    using a list of prompts specified in `prompt_chain`.
    """
    response_chain = []
    response = input_query
    for i, prompt in enumerate(prompt_chain):
        print(f"Step {i+1}")
        response = run_llm(f"{prompt}\nInput:\n{response}", model='meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo')
        response_chain.append(response)
        print(f"{response}\n")
    return response_chain

Example Usage

Python

question = "Sally earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?"

prompt_chain = ["""Given the math problem, ONLY extract any relevant numerical information and how it can be used.""",
                """Given the numberical information extracted, ONLY express the steps you would take to solve the problem.""",
                """Given the steps, express the final answer to the problem."""]

responses = serial_chain_workflow(question, prompt_chain)

final_answer = responses[-1]

Use cases

• Generating Marketing copy, then translating it into a different language.
• Writing an outline of a document, checking that the outline meets certain criteria, then writing the document based on the outline.
• Using an LLM to clean and standardize raw data, then passing the cleaned data to another LLM for insights, summaries, or visualizations.
• Generating a set of detailed questions based on a topic with one LLM, then passing those questions to another LLM to produce well-researched answers.