Do your solutions reliably find the people, places, and products in user request?
Entity recognition is a foundational capability for copilots, assistants, and transactional flows, but default NER behavior is often brittle, hard to govern, and difficult to scale.
To win, your GenAI solutions need to consistently identify and act on entities across real-world user requests.
The Challenge
Without a strong approach to entity recognition and mapping, teams struggle with:
- Inconsistent extraction — Default NER misses edge cases, synonyms, and domain-specific entities.
- Brittle integrations — Entities do not line up cleanly with how downstream APIs, services, and data models are defined.
- Significant quality issues — Entity gaps break workflows, personalization, and transaction flows in production.
Entity recognition gaps will significantly limit your ability to scale high-performing GenAI Solutions.
Our Solution
In this hands-on workshop, your team designs, implements, and validates robust entity extraction and mapping workflows using curated notebooks and datasets. Areas of focus include:
- Named Entity Recognition — reliably extract people, places, products, and key terms.
- Synonym Mapping & Thesaurus Integration — Normalize varied user language into a consistent, system-ready entity vocabulary.
- Entity-Based Action Mapping — Connect recognized entities to concrete actions, workflows, and downstream systems.
- Interactive Labs & Notebooks — Explore entity extraction and mapping behavior using curated Jupyter notebooks and sample requests.
- Capstone & Live Coaching — Assemble a complete entity-driven action flow and refine it with expert feedback and live debugging support.
Skills You'll Gain
- Production-Ready NER Pipelines — Design, implement, and validate end-to-end entity recognition and mapping workflows.
- Language Normalization Patterns — Build reusable synonym and vocabulary strategies aligned to your downstream systems.
- Entity-to-Action Design — Map entities cleanly into APIs, services, and data models for reliable automation.
- Failure Diagnosis & Hardening — Identify and fix entity-related edge cases before they impact user journeys.
- Faster Feature Delivery — Reuse entity patterns to accelerate new assistants, workflows, and transactional experiences.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
8 Hours
Skill Level
Intermediate NLP experience recommended
Tools
Jupyter notebooks + preconfigured entity/semantic modeling components
Explore the Remaining 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
Designing Interfaces for Seamless Data Use
Select Prioritizing Promising GenAI Ideas
Level 2 Item
Intent Detection
Disambiguation & Feedback
Semantic Analysis