Keywords AI
What is RAG?
Retrieval-Augmented Generation (RAG) is a design pattern that combines an information-retrieval system with a language model. Instead of relying solely on pre-trained parameters, a RAG pipeline fetches relevant documents at query time and injects them into the model’s prompt. This helps reduce hallucinations and keeps answers grounded in up-to-date, domain-specific knowledge.
Modern teams experiment with many variations on this pattern. Below are the top 8 RAG architectures to know in 2025, with workflows, use-cases, and pros & cons.
1. Simple RAG
What it is. Simple RAG is the original form of retrieval-augmented generation. The system converts the user’s query to a vector, looks up semantically similar documents in a vector database and feeds those documents plus the original question into a language model. There is no re-ranking or iterative retrieval.
Workflow
- Embed query & retrieve: Convert the query into an embedding and search the vector store for the top k relevant documents.
- Generate: Supply the retrieved snippets and the original query to the LLM for answer generation.
Use-cases
Simple RAG works well for FAQs, chatbots, or automation where the knowledge base is static and questions are straightforward.
Pros & cons
| Advantages | Drawbacks | |
|---|---|---|
| Simple RAG | Fast response times and low implementation cost | Struggles with multi-source questions and no feedback loop if retrieval quality is poor |
2. Simple RAG with Memory
What it is. This variant adds a memory module that retains previous interactions to improve retrieval for the current query.
Workflow
- Store context: Keep a memory of past queries and answers.
- Context-aware retrieval: Search both memory and the knowledge base for relevant information.
- Generate: Feed memory context and retrieved docs to the LLM.
Use-cases
Used in personal assistants, customer support bots, and tutoring systems where follow-up questions reference earlier topics.
Pros & cons
| Advantages | Drawbacks | |
|---|---|---|
| RAG with Memory | Reduces repetition and enables human-like interactions | Higher processing cost and potential privacy concerns |
3. Branched RAG
What it is. Branched RAG splits a single query into multiple sub-queries and explores them in parallel, then merges results.
Workflow
- Generate sub-queries.
- Parallel retrieval.
- Merge candidate answers.
Use-cases
Useful when queries span multiple domains (e.g., market research, competitor analysis).
Pros & cons
| Advantages | Drawbacks | |
|---|---|---|
| Branched RAG | Handles multi-intent questions and yields thoughtful responses | More complex orchestration and risk of overload |
4. HyDe (Hypothetical Document Embedding)
What it is. HyDe improves retrieval by generating a hypothetical document based on the query, embedding it, and then retrieving real documents that are semantically similar.
Workflow
- Generate hypothetical documents using an LLM.
- Embed & average them into a single vector.
- Retrieve real documents using that embedding.
- Generate final answer with retrieved docs.
Use-cases
Helps when queries are ambiguous, or when domain-specific vocabulary is missing from embeddings.
Pros & cons
| Advantages | Drawbacks | |
|---|---|---|
| HyDe | Improves recall for ambiguous/specialized queries | Adds computational cost and uses synthetic text, which can reduce transparency |
5. Adaptive RAG
What it is. Adaptive RAG analyzes the complexity of a query and routes it to the appropriate retrieval strategy — sometimes no retrieval, sometimes multi-step retrieval.
Workflow
- Query analysis (classify as simple, moderate, or complex).
- Route accordingly (direct answer, single retrieval, or multi-step retrieval).
- Generate answer with retrieved context.
Use-cases
Great for systems handling a wide range of queries (support bots, research tools).
Pros & cons
| Advantages | Drawbacks | |
|---|---|---|
| Adaptive RAG | Balances speed and depth, adjusts dynamically to query type | Requires classifiers and extra orchestration |
6. Corrective RAG (CRAG)
What it is. CRAG introduces a retrieval evaluator that scores retrieved documents and takes corrective action if results are poor.
Workflow
- Retrieve candidate docs.
- Evaluate them with a smaller model.
- If high-quality: refine and use.
- If low-quality: discard and web search.
- If mixed: blend refined retrieval with web search.
Use-cases
Best for high-stakes domains (law, medicine, finance) where retrieval quality must be guaranteed.
Pros & cons
| Advantages | Drawbacks | |
|---|---|---|
| Corrective RAG | Improves factual accuracy, detects and fixes poor retrievals | Slower and more resource-intensive |
7. Self-RAG
What it is. Self-RAG introduces self-reflection: the system decides when retrieval is needed, evaluates passage relevance, and critiques its own output.
Workflow
- Retrieve on demand (model decides when to fetch).
- Score relevance of retrieved passages.
- Critique outputs and select the best response.
Use-cases
Effective for long-form content, exploratory research, or dynamic Q&A where retrieval isn’t always necessary.
Pros & cons
| Advantages | Drawbacks | |
|---|---|---|
| Self-RAG | Retrieves only when needed, evaluates relevance, critiques answers | Requires special training and more complexity |
8. Agentic RAG
What it is. Agentic RAG blends RAG with autonomous agents that reason, plan, and act. The agent decides what information or actions are needed, retrieves dynamically, and iteratively improves answers.
Workflow
- Identify missing information or actions.
- Fetch via APIs, databases, or tools.
- Integrate with internal knowledge for generation.
- Refine through feedback loops.
Use-cases
Promising for multi-step reasoning (customer support, BI dashboards, clinical decision support, research assistants).
Pros & cons
| Advantages | Drawbacks | |
|---|---|---|
| Agentic RAG | Enables autonomy, proactive retrieval, and continuous learning | High implementation complexity and cost |
Choosing the Right Architecture
Selecting the right RAG architecture depends on your use case, data sources, and tolerance for complexity:
- Simple RAG → static KBs and simple queries.
- Memory RAG → conversational systems.
- Branched & HyDe → multi-domain or ambiguous queries.
- Adaptive → mixed workloads.
- Corrective & Self-RAG → accuracy-critical applications.
- Agentic → frontier use cases requiring planning and dynamic retrieval.
No matter which you choose, the principle is the same: retrieve first, then generate.
