LLM App
What is an LLM app?
An LLM app (large language model application) is a product or feature that uses an LLM to accomplish tasks, generate responses, or provide assistance to users. These applications leverage the text generation and understanding capabilities of large language models to deliver value through conversation, analysis, content creation, or decision support.
LLM apps differ from traditional software because they produce non-deterministic outputs—the same input might generate slightly different responses each time. This characteristic requires teams to approach quality assurance differently than they would for traditional deterministic code.
Why do LLM apps need traces?
To evaluate and improve an LLM app's quality, teams need traces—detailed records of user inputs and LLM responses. These traces provide the data foundation for understanding how the application performs in practice.
Without traces, teams can't effectively assess whether their LLM app works well. The traces capture real usage patterns, edge cases, and failure modes that wouldn't be obvious from theoretical design. Teams should implement data collection practices early, including user consent for storing traces, as this data becomes critical for systematic improvement.
How do teams build confidence in LLM apps?
Teams build confidence through evals—automated tests that verify the LLM app performs as expected. These evals work analogously to unit testing and integration testing in traditional software, giving teams systematic ways to measure quality.
Effective LLM app development involves fast feedback loops: analyze traces to identify error patterns, build evals for those error categories, improve prompts or logic, then run evals to verify improvements. This systematic approach allows teams to iterate rapidly and measure whether changes actually make the app better, rather than relying on subjective assessment or occasional spot checks.
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