QuickStart

What can TextGrad do? TextGrad can optimize your prompts from a language model in an automatic way.

import textgrad as tg

tg.set_backward_engine("gpt-4o", override=True)

# Step 1: Get an initial response from an LLM.
model = tg.BlackboxLLM("gpt-4o")
question_string = ("If it takes 1 hour to dry 25 shirts under the sun, "
                   "how long will it take to dry 30 shirts under the sun? "
                   "Reason step by step")

question = tg.Variable(question_string,
                       role_description="question to the LLM",
                       requires_grad=False)

answer = model(question)

answer.set_role_description("concise and accurate answer to the question")

# Step 2: Define the loss function and the optimizer, just like in PyTorch!
# Here, we don't have SGD, but we have TGD (Textual Gradient Descent)
# that works with "textual gradients".
optimizer = tg.TGD(parameters=[answer])
evaluation_instruction = (f"Here's a question: {question_string}. "
                           "Evaluate any given answer to this question, "
                           "be smart, logical, and very critical. "
                           "Just provide concise feedback.")


# TextLoss is a natural-language specified loss function that describes
# how we want to evaluate the reasoning.
loss_fn = tg.TextLoss(evaluation_instruction)