RAG Architectures Compared: From Basic RAG to Advanced Patterns

What is RAG and Why Does It Matter?

Retrieval-Augmented Generation (RAG) combines the strengths of Large Language Models with external knowledge. Instead of relying solely on knowledge learned during training, a RAG system can retrieve current, company-specific information and incorporate it into responses.

At innFactory, we deploy RAG systems for various use cases - from intelligent document chatbots to knowledge management systems for enterprises.

RAG Architectures Overview

1. Basic RAG (Naive RAG)

The simplest form consists of three steps:

1. Indexing: Documents are split into chunks and stored as vectors
2. Retrieval: For a query, the most similar chunks are found
3. Generation: The found chunks are passed to the LLM as context

Query → Embedding → Vector Search → Top-K Chunks → LLM → Answer

Advantages:

• Easy to implement
• Low latency
• Good for homogeneous document collections

Disadvantages:

• Limited relevance for complex queries
• No semantic weighting
• Problems with keyword-based searches

2. Hybrid Search RAG

Combines vector search with classic keyword search (BM25):

Query → [Vector Search + BM25 Search] → Fusion → Top-K → LLM → Answer

At innFactory, we frequently use Reciprocal Rank Fusion (RRF) to combine results from both search methods. This works particularly well for:

• Technical documentation with specific terms
• Mix of semantic and exact search queries
• Multilingual document collections

Technologies: Elasticsearch, OpenSearch, Weaviate, Qdrant

3. Re-Ranking RAG

Adds an additional evaluation layer:

Query → Retrieval (Top-100) → Re-Ranker Model → Top-K → LLM → Answer

The re-ranker (e.g., Cohere Rerank, BGE-Reranker) evaluates the relevance of each chunk to the query more accurately than the initial vector search.

When useful?

• Large document collections (>10,000 documents)
• High requirements for response quality
• Willingness to accept higher latency

4. Multi-Query RAG

The LLM generates multiple variants of the original query:

User Query → LLM (Query Expansion) → [Query 1, Query 2, Query 3]
→ Parallel Retrieval → Deduplication → LLM → Answer

This increases the probability of finding relevant documents, especially for:

• Ambiguous queries
• Domain-specific vocabulary
• Different formulations in documents

5. Agentic RAG

The most advanced architecture: An AI agent dynamically decides on the search strategy:

Query → Agent → [Decision: Which tools/data sources?]
      → Iterative Search → Evaluation → Possibly further search → Answer

The agent can:

• Choose between different data sources
• Iteratively refine search queries
• Validate results and search again if necessary
• Break down complex queries into substeps

Technologies: LangChain Agents, LlamaIndex Agents, AutoGPT Patterns

Architecture Decision Matrix

Our Recommendation

For most enterprise applications, we at innFactory recommend a staged approach:

1. Start with Hybrid Search RAG - Good balance of quality and complexity
2. Add Re-Ranking if quality is insufficient
3. Agentic Patterns only for complex multi-source scenarios

Conclusion

The choice of RAG architecture strongly depends on the use case. There is no “best” architecture - only the right one for your requirements. At innFactory, we analyze your data sources and requirements to identify the optimal architecture.

Planning a RAG project? Contact us for a non-binding initial consultation.