Types of RAG.

• Types of RAG

Naive RAG — The Basic Approach

• Naive RAG follows the simplest RAG pipeline: retrieve relevant information and give it to the LLM for generation.
• Documents are typically chunked, embedded, stored, and retrieved using similarity search.
• The retrieved chunks are added to the prompt as context for the LLM.
• It is simple to build and works well for straightforward question-answering tasks.
• Its main weakness is that poor retrieval can directly lead to poor answers.

Advanced RAG — Improving the Retrieval Pipeline

• Advanced RAG improves the basic pipeline with techniques before and after retrieval.
• Query rewriting, query expansion, better chunking, metadata filtering, and improved embeddings can increase retrieval quality.
• Retrieved documents can also be re-ranked to place the most relevant information first.
• Context compression can remove unnecessary information before it reaches the LLM.
• The goal is to make the same basic RAG pipeline more accurate, efficient, and reliable.

Modular RAG — Building RAG from Components

• Modular RAG breaks the RAG pipeline into independent components that can be combined and replaced.
• Different modules can handle retrieval, query rewriting, routing, re-ranking, generation, or memory.
• The system can use different workflows depending on the type of question.
• This makes RAG systems easier to customize for different business and technical requirements.
• Modular RAG moves RAG from a fixed pipeline toward a more flexible architecture.

Graph RAG — Connecting Knowledge Through Relationships

• Graph RAG represents information using entities and relationships rather than relying only on isolated text chunks.
• A knowledge graph can connect people, organizations, products, events, and concepts.
• Retrieval can then follow relationships between entities to discover connected information.
• This is particularly useful for questions requiring connections across multiple pieces of information.
• Graph-based retrieval can improve performance on complex, multi-hop questions.

Multimodal RAG — Beyond Text

• Multimodal RAG retrieves information from different data types such as text, images, tables, charts, audio, and video.
• Instead of treating a document as text only, the system can retrieve the most relevant visual or structured information.
• For example, a financial assistant could retrieve both the text explanation and the relevant chart from a report.
• This makes RAG useful for documents where important information is not represented purely as text.
• Multimodal RAG extends retrieval from a text-based problem to a multi-format knowledge problem.

Corrective RAG — Checking and Fixing Retrieval

• Corrective RAG introduces mechanisms to evaluate whether the retrieved information is actually useful.
• If retrieval quality is poor, the system can take corrective actions instead of blindly generating an answer.
• It may refine the query, retrieve additional information, or use another knowledge source.
• This reduces the risk of generating an answer from irrelevant or insufficient context.
• The key idea is: retrieve first, evaluate the evidence, and correct the retrieval when necessary.

Self-RAG — The Model Reflects on Retrieval

• Self-RAG allows the model to determine when retrieval is needed and reflect on the retrieved information.
• Instead of retrieving information for every query, the system can decide whether external knowledge is necessary.
• It can evaluate retrieved passages and assess aspects of its own generated response.
• This creates a more adaptive retrieval-and-generation process.
• The goal is to improve factuality and relevance while avoiding unnecessary retrieval.

Agentic RAG — RAG Meets AI Agents

• Agentic RAG combines retrieval with agent-style reasoning, planning, and tool use.
• Instead of following one fixed retrieval pipeline, the agent can decide what information it needs and where to find it.
• It can perform multiple retrieval steps, use different data sources, call tools, and refine its search based on intermediate results.
• This makes it suitable for complex tasks that require multi-step reasoning and information gathering.
• Agentic RAG represents a shift from "retrieve once and generate" toward "reason, retrieve, evaluate, and act."