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Use this when you already have a CSV of inputs and correct outputs for one repeatable LLM job. The steps below create a task, upload the rows, run prompt optimization, and print the score change.

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Install

pip install apprentice-sdk
LangChain support is optional. Install it only if you plan to capture from a LangChain app or pass a LangChain prompt: 
pip install "apprentice-sdk[langchain]"

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Set your keys

export APPRENTICE_API_KEY="ap_live_..."
export APPRENTICE_BASE_URL="https://your-backend"
Create the API key once in the console. The client also reads these two values from the environment, so you can leave them out of your code.

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Run it

import os
from apprentice import Apprentice

client = Apprentice(
    api_key=os.environ["APPRENTICE_API_KEY"],
    base_url=os.environ["APPRENTICE_BASE_URL"],
)

client.tasks.create("invoice-json", metric="json_f1")
client.datasets.upload(
    "invoice-json",
    path="golden.csv",
    input_col="input",
    output_col="output",
    prompt="Extract the invoice fields as a JSON object.",
)

report = client.optimize("invoice-json").wait().report()
print(report.baseline_score, "->", report.optimized_score)
print(report.optimized_prompt)
golden.csv needs an input column and an output column. For a JSON task, the output is the exact JSON you want back.

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What you get

report.baseline_score and report.optimized_score are field-level scores on rows the optimizer held out, so treat the result as proof for this dataset, not a universal benchmark. report.optimized_prompt is the rewritten instruction you can paste back into your app.
If optimized_score does not beat baseline_score, that is a real result, not a failure of the tool. Add cleaner verified rows, or pick a metric that fits the task, then run again. We never report a gain that is not there.

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Next

JSON extraction, end to end

The same flow with a real dataset and the optimized prompt pulled back into code.

Capture from LangChain

Build the dataset from your live traffic instead of a CSV.
Model replacement comes after this first prompt win. It is described on pages marked Building until the public flow ships.