RAG Architecture: Building Intelligent Knowledge Systems

Retrieval-Augmented Generation (RAG) has emerged as the go-to architecture for building AI systems that can reason over custom knowledge bases. This guide covers everything you need to know.

What is RAG?

RAG combines two powerful capabilities:

1. Retrieval – Finding relevant information from a knowledge base
2. Generation – Using an LLM to synthesize answers from retrieved context

This approach solves key LLM limitations:

• ✅ Up-to-date information (not limited to training cutoff)
• ✅ Domain-specific knowledge
• ✅ Source attribution and verification
• ✅ Reduced hallucinations

Core Components

1. Document Processing Pipeline

Raw Documents → Chunking → Embedding → Vector Store
     │              │           │            │
     └──────────────┴───────────┴────────────┘
               Ingestion Pipeline

Chunking Strategies:

• Fixed-size chunks (512-1024 tokens)
• Semantic chunking (by paragraph/section)
• Sliding window with overlap
• Hierarchical chunking

2. Embedding Models

Popular choices in 2025:

Model	Dimensions	Use Case
OpenAI text-embedding-3-large	3072	General purpose
Cohere embed-v3	1024	Multilingual
BGE-M3	1024	Open source
Jina embeddings v3	1024	Long context

3. Vector Databases

Options for storing and querying embeddings:

• Pinecone – Fully managed, highly scalable
• Weaviate – Open source, hybrid search
• Qdrant – Rust-based, high performance
• Chroma – Developer-friendly, lightweight
• pgvector – PostgreSQL extension

4. Retrieval Strategies

Basic Retrieval:

# Similarity search
results = vector_store.similarity_search(query, k=5)

Advanced Techniques:

• Hybrid search (keyword + semantic)
• Re-ranking with cross-encoders
• Multi-query retrieval
• Parent document retrieval
• Self-querying retrieval

Architecture Patterns

Basic RAG

User Query → Embed → Vector Search → Context + Query → LLM → Response

Advanced RAG

User Query
    │
    ├── Query Expansion (generate sub-queries)
    │
    ├── Hybrid Search (semantic + keyword)
    │
    ├── Re-ranking (cross-encoder scoring)
    │
    ├── Context Compression (extract relevant parts)
    │
    └── Generation (with citations)

Agentic RAG

User Query → Agent
              │
              ├── Plan retrieval strategy
              │
              ├── Execute searches (multi-hop)
              │
              ├── Evaluate results
              │
              └── Generate or iterate

Implementation Best Practices

Chunking

• Maintain semantic coherence
• Include metadata (source, date, section)
• Overlap chunks by 10-20%
• Consider document structure

Retrieval

• Tune k (number of results) based on context window
• Implement fallback strategies
• Cache frequent queries
• Monitor retrieval quality

Generation

• Structure prompts clearly
• Include source attribution
• Handle “I don’t know” gracefully
• Implement output validation

Evaluation Metrics

Measure RAG system quality:

1. Retrieval Metrics

   • Recall@k
   • Precision@k
   • Mean Reciprocal Rank (MRR)

2. Generation Metrics

   • Faithfulness (answer supported by context)
   • Relevance (answer addresses query)
   • Completeness (all aspects covered)

YUXOR RAG Solutions

Our RAG implementation services:

• Assessment – Evaluate your knowledge management needs
• Architecture Design – Custom RAG pipeline design
• Implementation – End-to-end development
• Optimization – Performance tuning and monitoring

Common Pitfalls

❌ Too small chunks – Lose context ❌ Too large chunks – Dilute relevance ❌ Ignoring metadata – Miss filtering opportunities ❌ No re-ranking – Return suboptimal results ❌ Poor prompt design – Inconsistent outputs

Conclusion

RAG architecture enables organizations to build AI systems that leverage their unique knowledge assets. Success requires careful attention to each component of the pipeline.

Build Your RAG System with YUXOR

Ready to build intelligent knowledge systems? YUXOR provides the tools you need:

1. Yuxor.dev - Access powerful embedding models and LLMs for RAG
2. Yuxor.studio - Build and deploy RAG applications with no-code tools
3. Custom Development - Let our team build your enterprise RAG solution

Start Building with Yuxor.dev and unlock your organization’s knowledge.

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