From naive to advanced RAG: the complete guide by Cédrick Lunven, Guillaume Laforge

Effective RAG implementations require careful consideration of chunking strategies - options include splitting by characters, sentences, or using recursive/hierarchical approaches with different chunk sizes for different content types

LLMs have limitations around training cutoff dates and context windows - RAG helps overcome these by retrieving relevant context from your own data sources

Vector similarity search methods like cosine similarity and dot product have different tradeoffs - cosine similarity is most common but dot product can be faster when vectors are normalized

Advanced RAG techniques include:

• Hypothetical document embedding
• Query transformations
• Re-ranking results using functions like RRF (Reciprocal Rank Fusion)
• Graph-based approaches for traversing related content
• Semantic chunking based on meaning rather than just size

Metadata is crucial for RAG systems - storing source info, timestamps, and chunk relationships helps with filtering and maintaining context

Vector databases need capabilities like:

• Efficient indexing (graph-based, HNSW etc.)
• Vector compression/quantization
• Metadata filtering
• Multi-vector search

Consider data lifecycle aspects:

• Document parsing and cleaning
• Chunking strategy selection
• Embedding model choice
• Re-embedding when content changes
• Security and access control

Performance optimization techniques include:

• Caching embeddings
• Using approximate nearest neighbor search
• Batch processing
• Query compression
• Smart chunking to reduce vector count

Evaluation metrics for RAG quality include:

• Recall
• Precision
• F1 score
• MRR and NDCG

The choice of embedding model impacts multilingual capabilities and domain-specific performance - consider fine-tuning for specialized use cases