Clearing & Parsing Your Data - Parsing & Tokenizing Your Data
Even high-quality data fails GenAI use cases if it isn’t parsed, chunked, and tokenized correctly for model inputs, context limits, and downstream reasoning.
To win, your GenAI solutions need data that is deliberately parsed, tokenized, and structured for LLM consumption.
When teams prepare data without GenAI-specific parsing strategies, they encounter:
- Unusable raw inputs: Structured and unstructured data is passed to models without consistent parsing logic.
- Context window failures: Poor chunking strategies truncate meaning or overwhelm model limits.
- Disconnected metadata: Parsed data loses critical context needed for grounding and retrieval.
Weak parsing and tokenization will limit model performance, increase hallucinations, and waste high-value data.
In this hands-on workshop, your team designs and applies parsing and tokenization strategies that make data usable, contextual, and reliable for GenAI models.
- Parse structured and unstructured data sources effectively.
- Tokenize data to meet LLM input requirements.
- Manage context windows and chunk data without losing meaning.
- Adapt parsing logic to different data types.
- Link parsed data to supporting metadata layers.
Parsing Structured and Unstructured Data
Tokenizing Data for LLM Input Readiness
Managing Context Windows and Data Chunks
Adapting Parsing Logic to Data Types
Linking Parsed Data to Metadata Layers
- Prepare data inputs that align with LLM processing constraints.
- Apply consistent parsing logic across varied data sources.
- Design chunking strategies that preserve semantic meaning.
- Reduce model errors caused by poor data segmentation.
- Maintain metadata connections that support grounded GenAI outputs.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
Parsing utilities, tokenization libraries, and curated GenAI preparation examples