User Input
What is user input?
User input refers to the data, query, or content that a user provides to an AI system. User input is foundational to all AI interactions—it's what triggers the AI to generate a response and shapes what that response will be.
In the context of AI products, user input appears in multiple contexts: traces capture user input alongside system responses for analysis, golden datasets pair specific user inputs with their desired outputs for evaluation, and RAG systems dynamically augment user input with additional context before processing.
Why is handling diverse user input challenging?
One of the key challenges in building AI products is ensuring consistent, high-quality responses across many different types of user input. Users vary widely in:
- How they phrase their queries
- The level of detail they provide
- The assumptions they make
- The edge cases they encounter
- Their intent and expectations
An AI system that works well for one type of user input might fail completely for another. Building robust AI products requires testing across diverse user inputs and handling edge cases gracefully.
How do RAG systems use user input?
In Retrieval-Augmented Generation (RAG) systems, user input serves as the starting point for a multi-step process. Rather than sending the user input directly to the LLM, RAG systems first use it to retrieve relevant information from a knowledge base or database.
For example, when a student submits an interview transcript for feedback, the system might use that user input to retrieve the student's feedback history. The LLM then processes both the original user input and the retrieved context together, enabling more informed, personalized responses.
This dynamic augmentation of user input allows AI systems to access information beyond what's in the LLM's training data.
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