Preparing Data
Data preparation is a critical step in ensuring your AI agents provide accurate and relevant responses. Properly prepared data improves retrieval accuracy, reduces hallucinations, and enhances the overall user experience.
Overview
Effective data preparation involves several key considerations:
Chunking: Breaking down large documents into manageable pieces
Synthetic Data: Enhancing your dataset with generated content
Data Manipulation: Transforming and enriching your data for better retrieval
Why Data Preparation Matters
The quality of your AI agent's responses is directly tied to the quality of your prepared data. Well-prepared data ensures:
Better Retrieval: Properly chunked data makes it easier for the system to find relevant information
Improved Accuracy: Clean, structured data reduces ambiguity and errors
Faster Responses: Optimized data structures enable quicker processing
Enhanced Context: Rich metadata and synthetic enhancements provide better context
Key Concepts
Document Processing
When you upload documents to Twig AI, they go through several processing stages:
Extraction: Text and metadata are extracted from various file formats
Chunking: Documents are split into smaller, semantically meaningful segments
Enrichment: Additional context and synthetic data may be added
Indexing: Processed chunks are indexed for efficient retrieval
Optimization Strategies
To get the most out of your data:
Choose appropriate chunking strategies for your content type
Generate synthetic Q&A pairs for better coverage
Add metadata to improve filtering and context
Regularly update and refine your data based on usage patterns
Getting Started
Assess Your Data: Understand the types and formats of your source data
Choose a Strategy: Select chunking and enrichment strategies that fit your use case
Process Your Data: Apply your chosen strategies to prepare your data
Test and Iterate: Monitor performance and adjust your approach as needed
Best Practices
Keep chunks focused: Each chunk should contain a complete thought or concept
Maintain context: Include enough surrounding information for standalone understanding
Use consistent formatting: Standardize how information is structured
Add rich metadata: Tags, categories, and timestamps improve retrieval
Monitor quality: Regularly review retrieved chunks to ensure relevance
Next Steps
Explore the following topics to dive deeper into data preparation:
Chunking Strategies - Learn how to effectively split your documents
Synthetic Data - Discover how to enhance your dataset
Data Manipulations - Master data transformation techniques
Last updated