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Prerequisites

  • You have already created an LLM evaluator. Learn how to create an LLM evaluator here.
  • You have already set up the Logging API or LLM proxy. Learn how to set up the Logging API here or LLM proxy here.

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Evaluate LLM output with Logging API

Once you have set up the Logging API, you can evaluate the LLM output with the Logging API.

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Required parameters

To run an evaluation successfully, you need to pass the following parameters:

  • completion_message: The completion message from the LLM.
  • eval_params: The parameters for the evaluator.
  • evaluators: The parameter under the eval_params.
  • evaluator_slug: The parameter under the evaluators, this is the slug of the evaluator you want to run.

Learn how to create an evaluator here.

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Example code

{
    "model": "openai/gpt-4o-mini",
    "prompt_messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "test"
        }
    ],
    "completion_message": {
        "role": "assistant",
        "content": "Hello, how are you?"
    },
    "eval_params": {
        "evaluators": [
            {"evaluator_slug": "rng-test"}
        ]
    }
    # ... other parameters
}

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Add ideal output in the code

ideal_output can be also said as the expected output. You can add it to let the evaluator know what is the expected/right output.

You need to add ideal_output to the description of the evaluator in the UI. Otherwise, the evaluator will not use the ideal_output from the code.

To add it in the code, you need to pass the ideal_output under the eval_inputs as shown in the example code below:

Example
{
    "eval_params": {
        "evaluators": [
            {
                "evaluator_slug": "rng-test"
            }
        ],
        "eval_inputs": {
            "ideal_output": "The answer is 3"
        }
    }
}

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Add multiple evaluators in the code

You can add multiple evaluators in the code. The evaluators will be run in parallel.

Example
{
    "eval_params": {
        "evaluators": [
            {"evaluator_slug": "rng-test"},
            {"evaluator_slug": "factual-correctness-demo"}
        ]
    }
}

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See the result

You can go to Logs and see the result of the evaluation in the side panel.

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Evaluate LLM output with LLM proxy

If you have set up the LLM proxy, you can evaluate the LLM output with the LLM proxy as well.

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Required parameters

To run an evaluation successfully, you need to pass the following parameters:

  • eval_params: The parameters for the evaluator.
  • evaluators: The parameter under the eval_params.
  • evaluator_slug: The parameter under the evaluators, this is the slug of the evaluator you want to run.

Learn how to create an evaluator here.

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Example code

{
"eval_params": {
    "evaluators": [
        {
            "evaluator_slug": "semantic-similarity-demo" # create it in the UI
        }
    ]
},
"model": "gpt-4o",
"messages": [
    {
        "role": "system",
        "content": "You are a seasoned marketing copywriter working for Keywords AI. Craft a personalized outreach email that weaves dynamic details into the message naturally. The placeholders are:• James – the recipient's first name• FinEdge – the company's name• financial technology and digital banking solutions – a brief about the company's focus• Product Manager – the recipient's position  \n\n Keywords AI is an LLM observability platform for AI startups, offering observability, prompt management, and evaluations to monitor, debug, and improve AI outputs. Maintain an engaging, warm tone."
    },
    {
        "role": "user",
        "content": "Compose a professional outreach email. Start by greeting James, then mention that FinEdge is recognized for its work in financial technology and digital banking solutions. Acknowledge the recipient's role as Product Manager and describe how Keywords AI's observability and management tools can improve their AI outputs. Conclude with an invitation to connect."
    }
],
}

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Add ideal output in the code

ideal_output can be also said as the expected output. You can add it to let the evaluator know what is the expected/right output.

You need to add ideal_output to the description of the evaluator in the UI. Otherwise, the evaluator will not use the ideal_output from the code.

To add it in the code, you need to pass the ideal_output under the eval_inputs as shown in the example code below:

Example
{
    "eval_params": {
        "evaluators": [
            {
                "evaluator_slug": "rng-test"
            }
        ],
        "eval_inputs": {
            "ideal_output": "The answer is 3"
        }
    }
}

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Add multiple evaluators in the code

You can add multiple evaluators in the code. The evaluators will be run in parallel.

Example
{
    "eval_params": {
        "evaluators": [
            {"evaluator_slug": "rng-test"},
            {"evaluator_slug": "factual-correctness-demo"}
        ]
    }
}

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How to run evals in other LLM SDKs

Evaluations parameters are similar to other Keywords AI parameters. Go to Integrations and find the LLM SDK you want to use.

For example, if you are using the OpenAI Python SDK, you can pass the evaluations parameters in extra_body.

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See the result

You can go to Logs and see the result of the evaluation in the side panel.