How effectively do your GenAI assistants handled semantic nuance?
Semantic analysis is foundational for GenAI assistants and copilots, turning messy, varied language into structured meaning that downstream systems can trust.
To win, your GenAI solutions need to extract and apply true semantic meaning from every user request.
The Challenge
Without a strong approach to semantic analysis, teams struggle to:
- Treat user inputs as flat text and depend on opaque model behavior to infer meaning.
- Align natural language requests with internal knowledge, taxonomies, and structured data.
- Support deeper reasoning steps, making systems hard to debug, extend, and trust.
Semantic analysis gaps will drive inconsistent answers, higher hallucination risk, and brittle GenAI behavior.
Our Solution
In this hands-on workshop, your team designs and implements knowledge-aware semantic analysis workflows using curated notebooks and datasets. Areas of focus include:
- Semantic Similarity & Paraphrase Detection — Build models that recognize when differently phrased user requests mean the same thing.
- Knowledge-Aware Semantics — Ground understanding in your organization’s graphs, taxonomies, and domain language.
- Semantic Role Labeling & Logical Forms — Extract structured roles and relationships to enable richer reasoning.
- Interactive Labs & Notebooks — Experiment with real examples in curated Jupyter environments.
- Capstone & Live Coaching — Assemble a full semantic analysis pipeline with expert feedback.
Skills You'll Gain
- Robust Semantic Understanding — Interpret paraphrased, vague, and multi-part user requests with confidence.
- Knowledge-Grounded Answers — Align responses with the right internal content and knowledge assets.
- Easier Debugging & Tuning — Use explicit semantic structure to trace, diagnose, and fix behavior.
- Reusable Semantic Patterns — Apply shared techniques across assistants, copilots, search, and workflows.
- Readiness for Advanced NLU — Support downstream reasoning, dialog management, and orchestration.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 Hours
Skill Level
Intermediate Python and basic NLP familiarity recommended
Tools
Jupyter notebooks plus preconfigured semantic analysis components
Explore our Understanding Natural Language User Requests Certification Workshops
Turn every GenAI interaction into a high-quality customer experience. Click below to explore the remaining workshops in the NLU certification series—and build the applied expertise your teams need to master NLU.
Input Parsing & Tokenization
Intent Detection
Entity Recognition
Disambiguation & Feedback