How Effectively Do Your GenAI Solutions Handle Unclear Requests?
Disambiguation and feedback are now foundational for safe, scalable assistants, but poorly designed flows can turn uncertainty into wrong answers, user frustration, and missed learning signals.
To win, your GenAI solutions need to recognize uncertainty, ask clarifying questions, and learn from every interaction.
The Challenge
Without a strong approach to disambiguation and feedback, teams struggle to:
- Control Model Guessing — Models “take a guess” off brittle confidence scores, ad hoc thresholds, or default replies.
- Manage Ambiguity States — Uncertainty, clarifications, and follow-ups aren’t tracked in a consistent dialog state.
- Harvest Learning — Ambiguous cases and user corrections aren’t captured to improve quality.
Weak disambiguation will drive wrong answers, erode user trust, and slow quality improvements.
Our Solution
In this hands-on workshop, your team designs and implements robust disambiguation, clarification, and feedback flows using curated notebooks and realistic ambiguity scenarios. Areas of focus include:
- Ambiguity Resolution & Contextual Hooks — Detect uncertainty and pull in the right context or signals before answering.
- Dialog State Tracking — Capture and manage conversation state so clarifications feel natural across turns.
- Scoring Mechanisms & Thresholds — Build scoring and threshold logic that triggers clarification instead of risky guesses.
- Feedback Loops & Continuous Improvement — Turn real interactions and corrections into continuous model and workflow updates.
- Interactive Labs, Capstone & Coaching — Explore flows in notebooks, assemble a working blueprint, and refine it with expert feedback.
Skills You'll Gain
- Safer Answering Behavior — Replace unsafe guesses with structured clarification flows at points of uncertainty.
- Stronger User Trust — Design follow-up questions that are clear, respectful, and confidence-building.
- Faster Learning Loops — Capture ambiguous cases as inputs to ongoing model, UX, and policy improvement.
- Production-Ready Disambiguation Patterns — Apply tested patterns for thresholds, prompts, routing, and fallbacks.
- More Reliable Multi-Step Journeys — Keep complex workflows stable even when user inputs are messy or incomplete.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 Hours
Skill Level
Intermediate, basic Python and GenAI familiarity
Tools
Jupyter notebooks plus preconfigured GenAI components
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