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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
- Decouple data processing stages with storage
- Choose technologies based on data structure and access patterns
- Leverage managed/serverless services when possible
- Use a log-centric design, storing raw data in S3
- 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