SE Radio 715: Sahaj Garg on Designing for Ambiguity in Human Input

2026-04-08 · Source: Software Engineering Radio - the podcast for professional software developers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Natural Language Processing · Depth: Intermediate, extended

Summary

Sahaj Garg, co-founder and CTO of Wispr, discusses designing robust AI systems to handle the inherent ambiguity in human input, covering text, voice, and multimodal data. The conversation defines ambiguity, distinguishing between inherent and reducible types, and categorizes it into lexical, syntactic, and pragmatic forms, with additional complexities in voice like homophones and accents. Garg details architectural and training strategies, including providing additional context, constructing datasets for annotation, and using instruction tuning. He also explores personalization through "revealed preferences" and methods to combat AI writing that "regresses to the mean." Finally, the discussion addresses communicating uncertainty to users without degrading experience and evaluating ambiguity resolution via offline and online signals.

Key takeaway

For AI Engineers building human-facing systems, you must prioritize context and user feedback to resolve input ambiguity. Implement mechanisms to learn from "revealed preferences," such as user corrections, to personalize outputs and prevent AI from "regressing to the mean." When uncertainty is unavoidable, communicate it gracefully with limited, actionable options, mirroring natural human interactions to maintain a positive user experience.

Key insights

Resolving AI system ambiguity requires more context and learning from user behavior.

Principles

• Ambiguity is resolved with more context.
• Learn user preferences from actions, not explicit feedback.
• Mirror human interaction patterns in system design.

Method

Train models with extra vocal/contextual information and noisy data. Use instruction tuning with synthetic and actual data to constrain generation space and align with desired behaviors, like formal vs. casual tone.

In practice

• Capture user corrections as implicit preference signals.
• Pre-fetch and compress context for faster inference.
• Offer limited, actionable choices to communicate uncertainty.

Topics

• AI Ambiguity Resolution
• Voice-to-Text AI
• Machine Learning Training
• User Personalization
• Instruction Tuning
• Human-Computer Interaction

Best for: NLP Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Software Engineering Radio - the podcast for professional software developers.