Paradigms of RAG Architectures
Retrieval-augmented systems are not built on a single pattern. Instead, they are categorized into four major architectural paradigms, ranging from simple linear pipelines to cyclic, stateful agent networks and graph-based indexing systems. Matching your application requirements to the correct paradigm is crucial for optimizing latency, development complexity, and search quality.
1. 🔀 Naive RAG Paradigm
Naive RAG is the baseline retrieval-augmented pattern. It operates as a stateless, single-turn lookup loop, fetching a fixed set of document chunks and feeding them directly to the language model without prior optimization or evaluation.
Naive RAG Pipeline Flow
Key Limitations
- Low Precision: The retrieval phase relies entirely on raw user queries, which are often poorly phrased, leading to irrelevant context chunks entering the prompt.
- Lost in the Middle: Feeding a flat, unranked block of text chunks into the context window causes the model to ignore critical information located in the middle of the prompt.
- Redundancy: Fails to check if the retrieved data actually answers the query, leading to generic or hallucinated responses when the database contains no relevant answers.
2. ⚡ Advanced RAG Paradigm
Advanced RAG introduces pre-retrieval (query transformation and metadata extraction) and post-retrieval (cross-encoder reranking and token compression) processing stages to optimize the context window before LLM synthesis.
Advanced RAG Pipeline Flow
Core Optimization Phases
- Pre-Retrieval: An LLM refines the raw user query, generating synonyms or breaking complex queries into multiple sub-queries. It also extracts filter tags (e.g. dates, categories) to prune the search index.
- Retrieval: The system executes a hybrid search, combining dense semantic vectors with sparse token BM25 indices, and fuses results using the Reciprocal Rank Fusion (RRF) algorithm.
- Post-Retrieval: A Cross-Encoder reranker scores the query-chunk pairs, sorting the candidates by true semantic relevance. The top-ranked chunks are compressed to strip redundant text, and injected into the prompt using XML delimiters.
3. 🤖 Agentic RAG Paradigm
Agentic RAG transitions the RAG pipeline from a deterministic workflow to a stateful, cyclic multi-agent loop. Rather than executing a predefined sequence, an orchestrator model dynamically plans retrieval steps, routes queries to multiple databases, evaluates context quality, and auto-corrects search queries on the fly.
Agentic RAG Stateful Loop
Operational Characteristics
- Dynamic Query Routing: The agent utilizes tool calling to select the most appropriate data connector based on semantic classification (e.g., querying Slack API for conversational context, Jira API for ticket updates, or a vector DB for design specifications).
- Reflective Verification (Self-Correction): The agent reviews intermediate outputs. If an evaluation check indicates that the retrieved document chunks do not satisfy the query, the agent adjusts parameters and initiates a new retrieval loop.
- State Persistence: The session’s state graph is checkpointed in a database, allowing the execution worker to pause and rehydrate if human-in-the-loop (HITL) gates are triggered for high-risk mutations.
🕸️ 4. GraphRAG (Knowledge-Graph-Based RAG)
Traditional vector-based retrieval excels at local similarity lookups (“What is the liability cap in Contract X?”), but fails on global, thematic queries (“Summarize the primary structural risks across all legal agreements in the database”). GraphRAG addresses this by indexing unstructured documents into a hierarchical knowledge graph.
Architectural Trade-offs: Vector RAG vs. GraphRAG
| Comparison Dimension | Vector-Based RAG | Graph-Based RAG (GraphRAG) |
|---|---|---|
| Primary Query Focus | Localized, specific details and specific document chunks. | Global, thematic summarization and corpus-wide connections. |
| Underlying Index Structure | Flat vector database collection (similarity distance search). | Hierarchical knowledge graph (nodes, edges, community summaries). |
| Indexing Pipeline Cost | Low (Simple chunking and single-pass embedding generation). | High (Recursive LLM prompts to extract entities, map relations). |
| Retrieval Latency | Very Low (Single similarity search turn). | Variable (Medium) (Graph traversal or community summary retrieval). |
| Data Integration | Best suited for flat, isolated documents. | Best for interconnected data (e.g. wikis, codebases, slack networks). |
GraphRAG Retrieval Modes
- Local Search: Queries are mapped to specific entities in the knowledge graph. The system retrieves the target entity node, its direct relationship edges, and parent community summaries to formulate an answer (ideal for detailed, entity-centric questions).
- Global Search: Queries are routed to pre-synthesized summaries of semantic “communities” (clustered entity groups). The LLM processes these community summaries in parallel, generating intermediate answers, and compiles them into a unified, corpus-wide global report (ideal for open-ended, thematic questions).
🔗 Related Sections
- Anatomy of RAG Systems — Technical component details, hybrid search code, and pgvector HNSW configurations.
- RAG vs Fine-Tuning — Strategic decision matrices comparing retrainings vs. context injections.
- Open Source RAG Tools — Framework comparisons (LlamaIndex, pgvector) and hybrid retrieval code.