16 Techniques to Supercharge and Build Real-world RAG Systems—Part 2

This article explores advanced techniques to enhance Retrieval-Augmented Generation (RAG) systems, focusing on improving retrieval quality, generation accuracy, and overall system performance. It covers methods like query transformation, hybrid search, fine-tuning embeddings, and post-retrieval optimization, providing practical implementation insights for real-world applications.

Core Technical Concepts/Technologies

• Retrieval-Augmented Generation (RAG)
• Query transformation (e.g., HyDE, step-back prompting)
• Hybrid search (dense + sparse retrieval)
• Embedding fine-tuning
• Re-ranking (e.g., Cross-Encoder, LostInTheMiddle mitigation)
• LLM-based evaluation metrics

Main Points

• Query Transformation:

  • HyDE (Hypothetical Document Embeddings): Generates hypothetical documents to improve retrieval by capturing query intent.
  • Step-Back Prompting: Asks the LLM to reason abstractly before retrieval for better context.

• Hybrid Search:

  • Combines dense (vector) and sparse (keyword) retrieval for balanced recall/precision.
  • Tools like Weaviate or Elasticsearch support hybrid approaches.

• Embedding Fine-Tuning:

  • Domain-specific tuning of embeddings (e.g., using SentenceTransformers) improves retrieval relevance.

• Re-ranking:

  • Cross-Encoder models (e.g., BERT) refine ranking post-retrieval.
  • "LostInTheMiddle" mitigation reorders documents to prioritize critical info.

• Evaluation:

  • LLM-based metrics (e.g., faithfulness, relevance) assess RAG outputs objectively.

Technical Specifications/Implementation

• Example: HyDE implementation with OpenAI embeddings:

  hypothetical_doc = llm.generate(f"Write a passage answering: {query}")  
  embedding = embed_model.encode(hypothetical_doc)  
  

• Re-ranking with Cross-Encoder:

  from sentence_transformers import CrossEncoder  
  cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")  
  scores = cross_encoder.predict([(query, doc) for doc in retrieved_docs])  
  

Key Takeaways

1. Query transformation (HyDE, step-back) significantly improves retrieval relevance.
2. Hybrid search balances precision/recall by combining vector + keyword methods.
3. Fine-tuned embeddings adapt RAG to domain-specific contexts.
4. Re-ranking (e.g., Cross-Encoder) refines results post-retrieval.
5. LLM-based evaluation ensures output quality aligns with real-world needs.

Limitations/Further Exploration

• HyDE performance depends on the base LLM’s quality.
• Hybrid search requires tuning weightage between dense/sparse methods.
• Cross-Encoder re-ranking adds computational overhead.
• Need for standardized RAG evaluation benchmarks.