Embedding Generation
Intermediate2+ years experienceAI/ML
Solid understanding with practical experience in multiple projects
My Experience
Expertise in generating and managing text embeddings for RAG systems and other AI applications. Experienced with various embedding models and optimization techniques.
Technical Deep Dive
Core Concepts I'm Proficient In:
• Model Selection: Choosing appropriate open-source embedding models for specific RAG use cases and performance requirements
• Text Chunking: Implementing intelligent text segmentation strategies with optimal chunk sizes and overlap for context preservation
• Preprocessing: Cleaning and normalizing text data before embedding generation to improve vector quality
• Batch Processing: Efficiently processing large document collections with optimized batch sizes for throughput
• Integration: Seamlessly integrating embedding generation into RAG pipelines with ChromaDB vector storage
• Quality Assurance: Validating embedding quality through similarity search accuracy and retrieval relevance metrics
Advanced Implementation Patterns:
• Open-Source Models: Leveraging cost-effective open-source embedding models (sentence-transformers, all-MiniLM) for production RAG systems
• Chunk Optimization: Fine-tuning chunk sizes (500-1000 characters) and overlap (100-200 characters) based on document type and query patterns
• Context Preservation: Implementing chunking strategies that maintain semantic coherence and prevent context loss at chunk boundaries
• Caching Strategies: Designing intelligent caching mechanisms to preserve relevant context across queries and reduce redundant embedding generation
• Performance Tuning: Optimizing embedding generation throughput (~4K characters/second) while maintaining quality
• Vector Normalization: Applying L2 normalization and other techniques to improve similarity search accuracy
Complex Problem-Solving Examples:
Notion RAG Embedding Architecture:
Engineered a comprehensive embedding generation pipeline for the [Notion RAG CLI tool](https://github.com/SamiMelhem/notion-rag-cli) that processes 54K+ characters of Notion content in ~14 seconds. Implemented intelligent chunking with 500-1000 character segments and 100-character overlap to preserve context across chunk boundaries, ensuring that retrieved chunks maintain semantic coherence for accurate RAG responses. The pipeline handles diverse content types from Notion blocks (paragraphs, lists, code blocks, tables) and normalizes them into uniform text representations suitable for embedding. Achieved ~1.4s average query response times through optimized embedding and retrieval strategies.
Context-Aware Chunking Strategy:
Developed an advanced chunking approach that goes beyond simple character-count splitting by analyzing document structure and preserving logical boundaries. Implemented overlap strategies that cache relevant context from previous chunks, allowing the RAG system to maintain continuity across long documents without losing critical details. This approach ensures that even when queries require information spanning multiple chunks, the system can reconstruct complete answers by intelligently combining related vector search results while maintaining the original context.
Areas for Continued Growth:
• Multi-Modal Embeddings: Exploring vision-language models and audio embeddings to build RAG systems that work across text, images, and audio
• Fine-Tuning: Learning techniques to fine-tune embedding models on domain-specific data for improved retrieval accuracy
• Advanced Chunking: Implementing semantic chunking strategies that adapt chunk boundaries based on document structure and content density
• Hybrid Retrieval: Combining dense embeddings with sparse retrievers (BM25) for improved search accuracy across different query types
Projects Using Embedding Generation
2+ years
Experience
1
Projects
Intermediate
Proficiency