LLM Response
What is an LLM response?
An LLM response is the output generated by a large language model in reply to user input. This response is what the LLM returns after processing the input, system prompts, and any additional context provided. The response is what users see and interact with, making it the primary output that determines the quality and usefulness of an AI product.
LLM responses are a core component of traces—records of AI interactions that include the user input, system prompts, and the model's output. In multi-turn conversations, traces capture all the back-and-forth exchanges between the user and the LLM.
How are LLM responses evaluated?
LLM responses can be evaluated through multiple methods. Code-based assertions check format and structure, such as verifying the response returns valid JSON or follows a required schema. This becomes particularly important in workflows where subsequent steps need to parse the output.
LLM-as-Judge evals assess quality and appropriateness by having a second LLM evaluate the first model's response against specific criteria. Human graders also review responses to establish ground truth for what constitutes a good or bad output, validating that automated evaluation methods work correctly.
Why do teams store and analyze LLM responses?
Storing LLM responses as part of traces is critical for improving AI products. By analyzing actual responses, teams can identify patterns in how the system performs, discover failure modes, and understand where quality issues occur.
These stored responses provide the data foundation for systematic improvement. Teams can run experiments by making changes to prompts or parameters, then compare new responses against previous ones using their evaluation framework. Without access to real LLM responses from actual usage, teams would struggle to measure quality or make informed decisions about improvements.
Learn more:
- Behind the Scenes: Building the Product Talk Interview Coach
- Building My First AI Product: 6 Lessons from My 90-Day Deep Dive
- How I Designed & Implemented Evals for Product Talk's Interview Coach
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