Agentic Design

Patterns
⚡

Advanced RAG(ARAG)

Enhanced RAG with pre-retrieval and post-retrieval optimizations including query expansion, reranking, and context curation

Complexity: mediumKnowledge Retrieval (RAG)

🎯 30-Second Overview

Pattern: Enhanced retrieval pipeline with query preprocessing, multi-stage retrieval, neural reranking, and context optimization

Why: Addresses limitations of naive RAG through query understanding, relevance scoring, and context quality optimization

Key Insight: Pre-retrieval optimization + post-retrieval processing significantly improves accuracy and relevance

⚡ Quick Implementation

1Pre-process:Query expansion, rewriting, routing
2Multi-retrieve:Multiple retrieval strategies and sources
3Rerank:Neural rerankers (BGE, Cohere, etc.)
4Filter:Relevance scoring and context selection
5Generate:Context-optimized generation with citations
Example: expand_query → multi_retrieve → neural_rerank → filter_context → generate_with_citations

📋 Do's & Don'ts

✅Implement query expansion (HyDE, query2doc)
✅Use neural rerankers (BGE-reranker, Cohere rerank)
✅Apply sentence window retrieval for context preservation
✅Implement relevance filtering with confidence thresholds
✅Use multiple embedding models for retrieval diversity
❌Skip query preprocessing and expansion
❌Rely solely on semantic similarity for ranking
❌Ignore document quality and freshness signals
❌Over-retrieve without proper filtering mechanisms
❌Neglect context window optimization

🚦 When to Use

Use When

  • • Production RAG systems requiring high accuracy
  • • Complex queries needing contextual understanding
  • • Large knowledge bases with noisy content
  • • Multi-domain or heterogeneous data sources
  • • Applications requiring source attribution

Avoid When

  • • Simple factual Q&A with clean data
  • • Resource-constrained environments
  • • Real-time applications (<100ms latency)
  • • Small knowledge bases with high-quality content
  • • Proof-of-concept or prototype systems

📊 Key Metrics

Retrieval Precision
Relevant docs in top-k after reranking
Answer Faithfulness
Generated content grounded in retrieved docs
Context Relevance
Retrieved context relevance to query
Reranking Effectiveness
NDCG@k improvement vs base retrieval
Query Understanding
Semantic similarity after expansion/rewriting
End-to-End Latency
Including pre-processing and reranking overhead

💡 Top Use Cases

Enterprise Search: Complex queries over large corporate knowledge bases with reranking
Legal Research: Multi-hop reasoning over case law with query expansion and relevance filtering
Medical Q&A: Clinical queries with domain-specific rerankers and confidence scoring
Technical Documentation: Developer queries with code-aware retrieval and context optimization
Research Assistant: Academic queries with citation tracking and multi-source retrieval

References & Further Reading

Deepen your understanding with these curated resources

Advanced RAG Surveys & Frameworks

Retrieval-Augmented Generation for Large Language Models: A Survey (Gao et al., 2023)

Seven Failure Points When Engineering a Retrieval Augmented Generation System (Barnett et al., 2024)

RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study (Ovadia et al., 2023)

A Comprehensive Survey of RAG: Evolution and Future Directions (Gupta et al., 2024)

Query Enhancement & Preprocessing

Hypothetical Document Embeddings (HyDE) for Retrieval (Gao et al., 2022)

Query2doc: Query Expansion with Large Language Models (Wang et al., 2023)

Rewrite-Retrieve-Read: Multi-Query Document Retrieval (Ma et al., 2023)

Active Retrieval Augmented Generation (Jiang et al., 2023)

Neural Reranking & Relevance Scoring

BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity (Chen et al., 2024)

FlagEmbedding: Dense Retrieval and Retrieval-augmented LLMs (BAAI, 2023)

RankLLaMA: Reranking with Large Language Models (Ma et al., 2023)

Cohere Rerank API Documentation and Best Practices

Context Optimization & Chunking

Lost in the Middle: How Language Models Use Long Contexts (Liu et al., 2023)

LongLLMLingua: Accelerating Large Language Model Inference via Prompt Compression (Jiang et al., 2023)

Sentence Window Retrieval: LlamaIndex Implementation

Recursive Character Text Splitter: LangChain Documentation

Implementation Frameworks & Tools

LlamaIndex Advanced RAG Techniques Documentation

LangChain Multi-Vector Retriever Implementation

Haystack Advanced Retrieval Pipelines

Weaviate Hybrid Search (Dense + Sparse) Documentation

Evaluation & Benchmarking

RAGAS: Automated Evaluation of RAG Applications

BEIR: Heterogeneous Benchmark for Information Retrieval (Thakur et al., 2021)

RGB: A Comprehensive Evaluation Benchmark for RAG Systems (Chen et al., 2024)

TruLens for RAG: Evaluation and Observability Framework

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