Prompt Compression and Query Optimization

What You'll Learn

• Integrate vector search with traditional database operations for efficient RAG applications.
• Use pre-filtering, post-filtering, and projection for faster query processing.
• Implement prompt compression to reduce prompt length in large-scale applications.

About This Course

This course focuses on combining database and vector search capabilities to optimize performance and cost-effectiveness in large-scale RAG applications. Key techniques covered include:

• Prefiltering and Postfiltering: Apply filters based on conditions, either at the index stage (prefiltering) or post-vector search (postfiltering).
• Projection: Limit returned fields from a query to reduce output size.
• Reranking: Reorder results to prioritize relevance using metadata fields.
• Prompt Compression: Optimize prompt length to improve processing cost-efficiency.

Hands-on exercises include implementing vector search with MongoDB, developing aggregation pipelines, and using metadata for refined searches.

Course Outline

• Introduction
  Overview of prompt compression and query optimization techniques.
• Vanilla Vector Search
  Implementing basic vector search capabilities for RAG applications.
• Filtering With Metadata
  Enhancing search with metadata-based filtering for improved relevance.
• Projections
  Selecting specific fields in query outputs to streamline data retrieval.
• Boosting
  Applying techniques to enhance query results based on metadata prioritization.
• Prompt Compression
  Techniques to reduce prompt length and optimize LLM processing costs.
• Conclusion
  Summary of techniques for efficient RAG and LLM applications.
• Appendix - Tips and Help
  Additional resources and code examples for advanced implementations.

Who Should Join?

This course is ideal for individuals with Python knowledge and a foundational understanding of databases and vector search who wish to optimize RAG applications.