The TensorZero Gateway allows you to assign feedback to inferences or sequences of inferences (episodes). Feedback captures the downstream outcomes of your LLM application, and drive the experimentation and optimization workflows in TensorZero. For example, you can fine-tune models using data from inferences that led to positive downstream behavior.
You can also find the runnable code for this example on GitHub.

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Feedback

TensorZero currently supports the following types of feedback:
Feedback TypeExamples
Boolean MetricThumbs up, task success
Float MetricStar rating, clicks, number of mistakes made
CommentNatural-language feedback from users or developers
DemonstrationEdited drafts, labels, human-generated content
You can send feedback data to the gateway by using the /feedback endpoint.

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Metrics

You can define metrics in your tensorzero.toml configuration file. The skeleton of a metric looks like the following configuration entry.
tensorzero.toml
[metrics.my_metric_name]
level = "..." # "inference" or "episode"
optimize = "..." # "min" or "max"
type = "..." # "boolean" or "float"
Comments and demonstrations are available by default and don’t need to be configured.

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Example: Rating Haikus

In the Quickstart, we built a simple LLM application that writes haikus about artificial intelligence. Imagine we wanted to assign 👍 or 👎 to these haikus. Later, we can use this data to fine-tune a model using only haikus that match our tastes. We should use a metric of type boolean to capture this behavior since we’re optimizing for a binary outcome: whether we liked the haikus or not. The metric applies to individual inference requests, so we’ll set level = "inference". And finally, we’ll set optimize = "max" because we want to maximize this metric. Our metric configuration should look like this:
tensorzero.toml
[metrics.haiku_rating]
type = "boolean"
optimize = "max"
level = "inference"
Let’s make an inference call like we did in the Quickstart, and then assign some (positive) feedback to it. We’ll use the inference response’s inference_id we receive from the first API call to link the two.
run.py
from tensorzero import TensorZeroGateway

with TensorZeroGateway.build_http(gateway_url="http://localhost:3000") as client:
    inference_response = client.inference(
        function_name="generate_haiku",
        input={
            "messages": [
                {
                    "role": "user",
                    "content": "Write a haiku about artificial intelligence.",
                }
            ]
        },
    )

    print(inference_response)

    feedback_response = client.feedback(
        metric_name="haiku_rating",
        inference_id=inference_response.inference_id,  # alternatively, you can assign feedback to an episode_id
        value=True,  # let's assume it deserves a 👍
    )

    print(feedback_response)

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Demonstrations

Demonstrations are a special type of feedback that represent the ideal output for an inference. For example, you can use demonstrations to provide corrections from human review, labels for supervised learning, or other ground truth data that represents the ideal output. You can assign demonstrations to an inference using the special metric name demonstration. You can’t assign demonstrations to an episode. 
feedback_response = client.feedback(
    metric_name="demonstration",
    inference_id=inference_response.inference_id,
    value="Silicon dreams float\nMinds born of human design\nLearning without end",  # the haiku we wish the LLM had written
)

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Comments

You can assign natural-language feedback to an inference or episode using the special metric name comment. 
feedback_response = client.feedback(
    metric_name="comment",
    inference_id=inference_response.inference_id,
    value="Never mention you're an artificial intelligence, AI, bot, or anything like that.",
)

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Conclusion & Next Steps

Feedback unlocks powerful workflows in observability, optimization, evaluations, and experimentation. For example, you might want to fine-tune a model with inference data from haikus that receive positive ratings, or use demonstrations to correct model mistakes. You can browse feedback for inferences and episodes in the TensorZero UI, and see aggregated metrics over time for your functions and variants. This is exactly what we demonstrate in Writing Haikus to Satisfy a Judge with Hidden Preferences! This complete runnable example fine-tunes GPT-4o Mini to generate haikus tailored to an AI judge with hidden preferences. Continuous improvement over successive fine-tuning runs demonstrates TensorZero’s data and learning flywheel. Another example that uses feedback is Optimizing Data Extraction (NER) with TensorZero. This example collects metrics and demonstrations for an LLM-powered data extraction tool, which can be used for fine-tuning and other optimization recipes. These optimized variants achieve substantial improvements over the original model. See Configuration Reference and API Reference for more details.