A practitioner’s guide to data for generative AI (DAT319)

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Key Takeaways

• Work backwards from the workflow and rag techniques to determine the data sources required for your generative AI application
• Leverage your existing data sources as much as possible, not just vector data
• Automate as much as possible to save time on operational tasks

Preparing Data for Rag Applications

Unstructured Data

• Need to chunk the data into bite-sized pieces that can be processed by the embedding model
• Techniques like fixed chunking, schematic chunking, hierarchical chunking, and semantic chunking can be used
• Trade-offs to consider include performance, relevancy, and cost

Structured and Semi-Structured Data

• Can be queried directly using natural language queries
• May need to transform into a vector representation to unlock additional semantic meaning

Index Selection

• Approximate Nearest Neighbor (ANN) indexes like HNSW are popular for vector search
• Need to balance factors like recall, storage size, and performance

Building the Rag Data Pipeline

Sources of Context

• Situational context from operational databases
• Semantic context from transformed data in vector databases
• Analytics context from data lakes/warehouses

Pipeline Design Principles

1. Decouple data processing stages with storage
2. Choose technologies based on data structure and access patterns
3. Leverage managed/serverless services when possible
4. Use a log-centric design, storing raw data in S3
5. Optimize for cost, not just raw scale

Data Sharing and Governance

• Establish a data product model to share data across teams
• Implement data quality checks and lineage tracking

Tying it All Together

• Focus on automating as much of the operational tasks as possible
• Leverage existing data sources, not just vector data
• Work backwards from the generative AI workflow and rag techniques