- Interpret intent
- Handle mixed input types
- Maintain accuracy across languages, acronyms and slang
In this hands-on workshop, your team will build a high-quality parsing and tokenization workflow step-by-step—leaving with a working pipeline you can adapt and reuse in your own environment.
Hands-on build: create an end-to-end parsing + tokenization workflow during the session
Practical outcomes: leave with a working pipeline—not just concepts and examples
Ready to apply: translate what you built into repeatable practices for your real data and tooling
- Parsing Inputs with Traditional and LLM-Based Tools — Learn approaches for robust, flexible input parsing.
- Multi-Language Parsing — Understand how to break down text reliably across linguistic structures.
- Handling Mixed Modal Inputs — Work with combined text, symbols, and multi-format inputs.
- Understanding Limitations of Tokenization — Recognize where token-based systems break down and how to mitigate issues.
- Preparing Parsed Data for Intent Detection — Transform parsed content into structured inputs ready for classification.
- A Strong Parsing Foundation — Build accurate, adaptable parsing pipelines for real-world scenarios.
- Hands-On Pipeline Experience — Leave having implemented a working parsing and tokenization flow.
- Better Downstream Accuracy — Improve reliability in intent detection, entity recognition, and retrieval.
- Reusable Parsing Patterns — Gain methods you can apply across future NLP workflows.
- Higher Readiness for Advanced NLU Projects — Equip your team with skills to support multi-turn conversations and complex workflows.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 Hours
Basic Python and NLP familiarity recommended
Jupyter notebooks + preconfigured parsing and tokenization components
Explore the remaining NLU 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.
