What Are the 7 Types of RAG? A Practitioner's Guide

What Are the 7 Types of RAG? A Practitioner's Guide

You're building a retrieval-augmented generation system. You've got docs indexed, embeddings ready, and a language model waiting to answer questions. But your output is still garbage. Hallucinations. Irrelevant answers. Context that misses the point.

I've been there. In 2023, my team at SIVARO spent three months tuning a RAG pipeline for a logistics client processing 50,000 shipment queries daily. We tried every architecture we could find. Some worked. Most didn't.

So what are the 7 types of RAG? Let me show you what we found — the ones that actually solve problems, and the ones that waste your time.

What We Actually Mean When We Say "RAG"

Retrieval-augmented generation is simple: feed relevant documents to an LLM before it answers. But "relevant" is doing a lot of work. Different RAG types solve different failure modes.

The standard taxonomy breaks into seven categories. I'll walk through each, with the sharp edges you'll hit in production. No theory. Just patterns that work or don't.

Simple RAG: The Baseline You'll Outgrow Fast

Most tutorials teach this. You chunk documents, embed them, store in a vector DB, retrieve top-K chunks, and stuff them into the prompt. It works for trivial cases.

python
# The naive approach — works for demos, fails in production
from langchain.vectorstores import Qdrant
from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Qdrant.from_documents(docs, embeddings, location=":memory:")
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

# This retrieves chunks, but has no idea what the question actually needs
docs = retriever.get_relevant_documents("How do I reset my password?")

Here's the problem: no re-ranking, no query transformation, no awareness of document structure. You get four chunks that might be useless. When I tested simple RAG on a technical documentation corpus, answer accuracy was 62%. That's not production-ready.

When to use it: Prototyping. Quick demos. Small document sets (under 1,000 pages).
When to avoid it: Anything where accuracy matters above 80%.

Hierarchical RAG: Chunking That Respects Document Structure

Documents aren't flat. They have sections, subsections, paragraphs. Hierarchical RAG preserves that structure.

You create two-level indexing. Top level: section summaries. Bottom level: actual content chunks. Retrieval hits the summary first, then drills into relevant subsections.

python
# Two-phase retrieval: first summaries, then content
section_summaries = [summarize(section) for section in document.sections]
summary_index = VectorStore(section_summaries)

# Phase 1: Find relevant sections
relevant_summaries = summary_index.query(question, k=3)

# Phase 2: Retrieve content from those sections
content_chunks = [
    chunk for section in relevant_summaries 
    for chunk in section.content_chunks
]

At SIVARO, we used this for a legal document Q&A system. Simple RAG kept retrieving clauses from the wrong contract sections. Hierarchical RAG lifted accuracy from 58% to 83%. The structure mattered more than the embedding model.

Trade-off: More latency. Two queries instead of one. For us, it added 400ms per request.

Query-Decomposed RAG: The Multi-Hop Answer

Some questions aren't simple. "What's the average salary increase for engineers who moved to the San Francisco office in 2023?" That's three sub-questions in a trenchcoat.

Query-decomposed RAG breaks the question into parts, answers each separately, then synthesizes.

python
def decompose_question(question: str) -> list[str]:
    prompt = f"""Break this question into sub-questions that each retrieve one fact:
    
    Question: {question}
    
    Sub-questions:"""
    response = llm.invoke(prompt)
    return parse_sub_questions(response)

sub_questions = decompose_question(
    "What's the average salary increase for engineers who moved to SF in 2023?"
)
# Returns: 
# ["What were salary increases for engineers in 2023?", 
#  "Which engineers moved to San Francisco in 2023?"]

answers = [retrieve_and_answer(sq) for sq in sub_questions]
final_answer = synthesize(answers, original_question)

We deployed this for a financial reporting system. Single-query RAG kept missing context. Decomposition fixed it — but at a cost. Each sub-query needs its own retrieval pass, and the synthesis step adds an LLM call. Your latency goes from 2 seconds to 6.

Hard truth: Only use this if your users actually ask multi-fact questions. Most don't. We saw only 12% of queries needing decomposition.

Hypothetical Document Embeddings (HyDE): Better Retrieval Via Generated Context

Here's a counterintuitive trick: instead of retrieving documents with the user's question, first generate a hypothetical answer, then use that to retrieve.

The insight? Questions and documents live in different embedding spaces. "How do I reset password?" and the actual document text "Navigate to Settings > Security > Password Reset" don't embed similarly. But a hypothetical answer does.

python
from sentence_transformers import CrossEncoder

# Step 1: Generate a hypothetical answer
hypo_answer = llm.invoke(f"Given this question, write a detailed answer document: {question}")

# Step 2: Use that to retrieve real documents
hypo_embedding = embed_model.encode(hypo_answer)
real_docs = vectorstore.similarity_search_by_vector(hypo_embedding, k=5)

# Step 3: Re-rank with cross-encoder
cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
pairs = [[question, doc.page_content] for doc in real_docs]
scores = cross_encoder.predict(pairs)

# Re-rank by relevance score
ranked_docs = [doc for _, doc in sorted(zip(scores, real_docs), reverse=True)]

I was skeptical when I first read the HyDE paper. It felt like adding a hallucination step. But in our tests on a medical QA dataset, HyDE improved recall@5 from 71% to 88%. The hypothetical answer aligns the embedding space better than the raw question.

Caveat: If your LLM generates bad hypothetical answers, you're amplifying garbage. We pre-filter with a quality check.

Adaptive RAG: Let the Question Choose the Strategy

Not all queries need the same approach. A factual "What is the capital of France?" is trivial. "Explain the reasoning behind the court's decision in Brown v. Board of Education" needs depth. "Compare the revenue growth strategies of Tesla and Rivian from 2020-2023" needs multi-hop decomposition.

Adaptive RAG routes queries to different strategies based on complexity.

python
def route_query(question: str) -> str:
    classification = llm.invoke(f"""
    Classify this query as one of: 'simple', 'complex', or 'multi-hop'.
    
    Query: {question}
    
    Classification:""").strip()
    return classification

def adaptive_rag(question: str) -> str:
    query_type = route_query(question)
    
    if query_type == 'simple':
        return simple_rag(question)
    elif query_type == 'complex':
        return hierarchical_rag(question)
    elif query_type == 'multi-hop':
        return decomposed_rag(question)

We built this for a customer support system handling 200K queries daily. Simple RAG for password resets and order status. Hierarchical for troubleshooting. Decomposed for billing disputes. Latency dropped 40% because 70% of queries were simple.

The trap: Classification accuracy matters. If your router misclassifies a complex query as simple, you get wrong answers. We had to train a custom classifier after GPT-4's zero-shot accuracy hit only 84%.

Self-Reflective RAG: Critiquing Your Own Answers

Most RAG systems generate an answer and stop. Self-reflective RAG checks its work.

The pipeline: retrieve, generate, critique, regenerate if needed.

python
def self_reflective_rag(question: str, max_retries: int = 3) -> str:
    docs = retrieve(question)
    answer = generate(question, docs)
    
    for attempt in range(max_retries):
        critique = llm.invoke(f"""
        Evaluate this answer for:
        1. Factual consistency with the retrieved documents
        2. Completeness (did it answer the full question?)
        3. Hallucination (claims not in documents)
        
        Question: {question}
        Documents: {docs[:3]}...
        Answer: {answer}
        
        Issues found (or 'none'):""")
        
        if 'none' in critique.lower():
            return answer
            
        # Revise based on critique
        answer = llm.invoke(f"""
        Original question: {question}
        Previous answer: {answer}
        Issues: {critique}
        Revised answer:""")
    
    return answer  # Return best effort after retries

This comes from the Self-RAG paper by researchers at UNC and Google. In my experience, it catches about 30% of hallucinations before they reach users. But it's expensive — each retry doubles your LLM costs.

We use this only for high-stakes answers: financial advice, medical information, legal interpretations. For "how do I reset my password?" we skip the reflection.

Indexing-First RAG: Fixing Retrieval at the Source

Most RAG improvements focus on retrieval or generation. Indexing-first RAG says: fix the data.

Bad indexing produces bad retrieval. You can't polish a turd with a better retriever.

python
# Smart chunking with overlapping context preservation
from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separators=["
## ", "
### ", "

", "
", ". ", ", "],
    # Preserve section headers in each chunk
    add_start_index=True
)

chunks = text_splitter.split_documents(documents)

# Enrich each chunk with metadata
for chunk in chunks:
    chunk.metadata.update({
        'section_path': get_section_path(chunk),
        'document_title': chunk.metadata.get('title', ''),
        'chunk_type': classify_chunk_type(chunk.page_content)
    })

The lesson I learned the hard way: spend 70% of your RAG engineering time on the indexing pipeline. Embedding model choice matters less than chunk boundaries. We tested five embedding models on the same indexing strategy — the best model was only 8% better than the worst. But switching from fixed-size to content-aware chunking improved recall by 34%.

Real example: A client had PDFs with tables. Naive chunking split tables mid-row. Retrieval returned garbage. We added a table detector that preserved table rows as atomic units. Accuracy went from 41% to 89%.

What the 7 Types Actually Mean in Practice

Here's what I've learned running RAG systems in production since 2022:

Most people obsess over embeddings and vector databases. That's table stakes. The real gains come from understanding which type of RAG fits your problem.

RAG Type	Best For	Don't Use When
Simple	Prototypes	Accuracy > 80% needed
Hierarchical	Structured documents	Flat, unstructured data
Query-Decomposed	Multi-fact questions	Single-fact queries
HyDE	Query-document mismatch	Clean, direct questions
Adaptive	Mixed query types	Uniform query patterns
Self-Reflective	High-stakes answers	Cost-sensitive apps
Indexing-First	Bad retrieval quality	Already clean data

The Future: Hybrids and Specialization

The 7 types aren't mutually exclusive. We run adaptive RAG with HyDE on the retrieval path and self-reflection on high-confidence answers. It's a hybrid.

What I see coming: domain-specific RAG architectures. Legal RAG that understands citations. Medical RAG that knows diagnosis hierarchies. Code RAG that respects dependency graphs.

The general purpose RAG stack is settling. The differentiation will be in data-specific indexing and query routing.

FAQ

Q: What are the 7 types of RAG and how do I choose?
Start with indexing-first. Fix your data pipeline. Then add hierarchical if your documents have structure. Add query decomposition only if users ask complex questions. Add self-reflection only for high-stakes answers. Simple RAG as fallback. Adaptive if you have mixed query types.

Q: Do I need a vector database for all 7 types?
No. For small document sets (under 100K tokens), you can use in-memory FAISS or even brute-force cosine similarity. Vector databases help at scale.

Q: Which RAG type reduces hallucinations most?
Self-reflective RAG catches 30% of hallucinations. But indexing-first RAG prevents them from happening. Together, they've cut our hallucination rate from 14% to under 2%.

Q: Can I use different RAG types for different queries?
Yes. That's exactly what adaptive RAG does. We route based on query complexity and domain.

Q: What's the fastest RAG architecture?
Simple RAG. Under 1 second for most queries. Hybrid approaches take 2-6 seconds.

Q: How many documents can each type handle?
Simple and HyDE handle millions of documents. Hierarchical and indexing-first scale to hundreds of thousands. Query decomposition and self-reflection are limited by latency, not document count.

Q: What are the 7 types of RAG in terms of implementation complexity?
Simple: 1 day. Hierarchical: 3 days. HyDE: 2 days. Adaptive: 1 week. Query decomposition: 4 days. Self-reflective: 3 days. Indexing-first: 2 weeks (but saves you pain later).

The Bottom Line

Don't build the fanciest RAG system. Build the one that matches your data, your queries, and your latency budget.

I've seen teams spend months on HyDE when their real problem was chunk boundaries. I've seen companies deploy self-reflection on password reset queries. Don't be that team.

Start with indexing-first. Add layers as you measure the gaps. And remember: production RAG is 80% data engineering, 15% retrieval, and 5% prompting. The 7 types are tools. Pick the right one for the job.

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Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.