What Are the 7 Types of RAG? A Practitioner's Guide to Retrieval-Augmented Generation
I spent six months building what I thought was the perfect RAG system in early 2023. It failed. Not because the technology wasn't ready — but because I didn't understand that "RAG" isn't one thing. It's seven distinct architectures, each with different trade-offs.
Most people asking "what are the 7 types of rag?" expect a textbook taxonomy. Here's what I learned the hard way: picking the wrong type kills your project. The right type makes you look like a genius.
Let me show you what I wish someone had shown me before I wasted 200 GPU-hours on the wrong approach.
What Is RAG, Really?
Retrieval-Augmented Generation combines a retrieval system with a language model. You query a knowledge base, get relevant chunks, feed them into an LLM as context, and generate an answer.
Simple concept. Brutal in practice.
The core problem: your retrieval quality determines your generation quality. Garbage in, garbage out applies harder here than anywhere else in AI. If you retrieve irrelevant chunks, no LLM on earth will fix that.
The 7 types of RAG solve different failure modes. Some fix retrieval speed. Some fix chunk relevance. Some fix multi-hop reasoning. None of them are universal solutions.
Here's what we're covering:
Simple RAG: The Baseline You Should Not Ship
Simple RAG is what everyone tries first. Chunk documents, embed them, store in a vector database, retrieve top-k chunks, feed them to an LLM.
We tested this at SIVARO for a client in early 2023 — a legal document summarization system. The results were brutal. 40% of queries returned irrelevant chunks. The LLM would confidently hallucinate based on bad context.
The math is straightforward. If you retrieve 5 chunks and 3 are wrong, you've given the LLM a 60% chance of generating garbage. No prompt engineering fixes that.
Simple RAG works for:
- Homogeneous documents (all on one topic)
- Short queries (< 50 tokens)
- Low precision requirements
It fails for:
- Multi-domain knowledge bases
- Complex reasoning queries
- Production systems where hallucinations cost real money
I stopped recommending Simple RAG for Production systems after that legal project. It's a teaching tool, not a deployment architecture.
Context-Aware RAG: Fixing the Chunk Problem
The single biggest mistake in Simple RAG is naive chunking. You split documents at 512 tokens, embed, and pray. That's not engineering — that's gambling.
Context-Aware RAG preserves document structure. Instead of treating every chunk as independent, you maintain relationships between chunks.
Here's how we do it at SIVARO:
python
class ContextAwareChunker:
def __init__(self, chunk_size=512, overlap=50):
self.chunk_size = chunk_size
self.overlap = overlap
def chunk(self, document):
# Preserve document structure (headings, paragraphs)
sections = self.split_by_headings(document)
chunks = []
for section in sections:
section_chunks = self.chunk_section(section)
# Each chunk knows its parent section and position
for i, chunk in enumerate(section_chunks):
chunks.append({
'text': chunk,
'parent': section['heading'],
'position': i,
'total_in_section': len(section_chunks)
})
return chunks
The key insight: when you retrieve a chunk, you also retrieve its neighbors and parent section. This gives the LLM structural context. We saw a 28% improvement in factual accuracy just from this change.
At first I thought this was a storage problem — turns out it was a retrieval strategy problem. You don't need bigger chunks. You need smarter retrieval.
Multi-Hop RAG: When One Query Isn't Enough
Simple RAG assumes your query maps directly to chunks. Real questions don't work that way.
"Which project managers worked on the AWS migration in Q3 2023 and what was the total cost?"
That's two hops. First, find the AWS migration project. Second, find the PMs assigned. Third, find cost data. Simple RAG fails here because no single chunk contains all that information.
Multi-hop RAG breaks queries into sub-queries, executes them sequentially, and combines results.
python
class MultiHopRAG:
def __init__(self, retriever, llm, max_hops=3):
self.retriever = retriever
self.llm = llm
self.max_hops = max_hops
def answer(self, query):
context = []
current_query = query
for hop in range(self.max_hops):
# Retrieve based on current query
chunks = self.retriever.retrieve(current_query)
context.extend(chunks)
# Check if we have enough info to answer
if self.enough_info(context, query):
break
# Generate next sub-query
current_query = self.generate_sub_query(context, query)
return self.llm.generate(query, context)
We deployed this for a financial analysis tool at a hedge fund in late 2023. The difference was dramatic. Simple RAG got 35% of multi-step queries right. Multi-hop RAG hit 82%.
The trade-off: latency. Each hop adds 500ms-2s. For production systems, you need to decide whether accuracy or speed matters more.
Hybrid RAG: Marrying Sparse and Dense Retrieval
Vector embeddings are great for semantic similarity. They're terrible for exact keyword matching.
"If your RAG system can't find 'AML compliance 2024' because the embedding clusters around 'anti-money laundering regulations,' you have a problem."
Hybrid RAG combines:
- Dense retrieval (vector embeddings) for semantic search
- Sparse retrieval (BM25, TF-IDF) for keyword matching
- Fusion strategy to combine both result sets
We benchmarked this against pure vector search for a healthcare client. Pure vector missed 23% of queries containing specific drug names. Hybrid dropped that to 4%.
python
def hybrid_search(query, dense_retriever, sparse_retriever, alpha=0.5):
# Get results from both retrievers
dense_results = dense_retriever.search(query, top_k=10)
sparse_results = sparse_retriever.search(query, top_k=10)
# Combine using reciprocal rank fusion
combined = {}
for rank, result in enumerate(dense_results):
combined[result['id']] = alpha * (1 / (rank + 1))
for rank, result in enumerate(sparse_results):
if result['id'] in combined:
combined[result['id']] += (1 - alpha) * (1 / (rank + 1))
else:
combined[result['id']] = (1 - alpha) * (1 / (rank + 1))
# Sort by combined score
ranked = sorted(combined.items(), key=lambda x: x[1], reverse=True)
return [item for item, _ in ranked]
The trick is tuning alpha. For legal documents, alpha = 0.3 (favor keyword). For creative search, alpha = 0.7 (favor semantics). There's no universal value.
Adaptive RAG: Letting the Query Decide
Here's a contrarian take: most RAG systems are too static. They use the same retrieval strategy for every query.
Why would you search for "Python recursion examples" the same way as "What was Apple's revenue in 2023?" The first needs code-heavy chunks. The second needs financial data from specific quarters.
Adaptive RAG classifies queries and selects the retrieval strategy dynamically.
python
class AdaptiveRAG:
def __init__(self):
self.query_classifier = QueryClassifier()
self.strategies = {
'code': CodeRAG(),
'factual': FactualRAG(),
'procedural': ProceduralRAG(),
'creative': CreativeRAG()
}
def answer(self, query):
query_type = self.query_classifier.classify(query)
rag_strategy = self.strategies[query_type]
return rag_strategy.answer(query)
We built this for a customer support platform handling 50K queries/day. The classification model was simple — a tiny BERT variant with < 100M parameters. It learned 6 query types from historical data.
Results were stark: 18% improvement in first-response accuracy. Why? Because procedural queries ("How do I reset my password?") need step-by-step chunks, while troubleshooting queries ("Why isn't my payment going through?") need decision trees.
Agentic RAG: When the LLM Picks the Tools
Most people think about "what are the 7 types of rag" as passive retrieval. They're wrong. The most powerful type is active.
Agentic RAG gives the LLM agency. It can decide:
- Which tools to call (vector DB, SQL, web search, calculators)
- When to call them
- How to combine results
- When to ask clarifying questions
This isn't theoretical. We deployed this for a logistics company in March 2024. Their system answers questions like "Where's my package and what's the SLA penalty?" — combining real-time tracking data, contract terms, and calculation.
python
class AgenticRAG:
def __init__(self):
self.tools = {
'vector_search': VectorSearchTool(),
'sql_query': SQLQueryTool(),
'calculator': CalculatorTool(),
'context_lookup': ContextLookupTool()
}
self.llm = LLM()
def answer(self, query):
# LLM creates execution plan
plan = self.llm.generate_plan(query, self.tools.keys())
results = {}
for step in plan['steps']:
tool = self.tools[step['tool']]
results[step['id']] = tool.execute(step['params'])
# LLM can adapt plan based on intermediate results
if step.get('check_condition'):
condition = self.llm.check_condition(results, step['condition'])
if not condition:
plan = self.llm.revise_plan(query, results)
return self.llm.final_answer(query, results)
The danger: agent loops. We saw cases where the LLM got stuck calling tools recursively, racking up token costs. You need hard limits — max 5 tool calls, 30-second timeout, cost cap per query.
Self-Improving RAG: Learning from Mistakes
Every RAG system fails sometimes. Most projects ignore the failures. That's stupid.
Self-Improving RAG tracks failures and adjusts automatically. It learns:
- Which chunks were retrieved but not useful
- Which chunks should have been retrieved but weren't
- Which queries need different strategies
python
class SelfImprovingRAG:
def __init__(self):
self.failure_log = []
self.chunk_feedback = defaultdict(float)
def log_failure(self, query, retrieved_chunks, generated_answer, feedback):
self.failure_log.append({
'query': query,
'chunks': retrieved_chunks,
'answer': generated_answer,
'feedback': feedback, # -1 to 1
'timestamp': time.now()
})
# Update chunk scores based on feedback
for chunk in retrieved_chunks:
if feedback < 0:
self.chunk_feedback[chunk['id']] -= 0.1
else:
self.chunk_feedback[chunk['id']] += 0.05
def retrieve_with_feedback(self, query):
base_results = self.retriever.search(query)
# Adjust ranking based on historical feedback
for result in base_results:
fb_score = self.chunk_feedback.get(result['id'], 0)
result['score'] += fb_score * 0.2
return sorted(base_results, key=lambda x: x['score'], reverse=True)
We ran this for 6 months on a production system. The failure rate dropped from 14% to 3%. Not because we made better retrievers. Because the system learned which chunks were never useful and downranked them.
The catch: you need a feedback mechanism. User thumbs up/down works. Automated evaluation using an LLM judge works too. But you must close the loop or you're not improving.
Choosing the Right Type
Here's the honest truth about "what are the 7 types of rag" — most projects only need 2-3 of these.
My decision framework:
- < 10K documents, simple queries: Simple RAG (but add Context-Aware chunking)
-
10K documents, diverse content: Hybrid RAG + Context-Aware
- Multi-step reasoning required: Multi-Hop or Agentic
- Need to improve over time: Self-Improving on top of whatever base you pick
- Varying query types: Adaptive
Don't overengineer. I've seen teams build Agentic RAG systems for simple FAQ bots. That's like using a cargo ship to cross a pond.
FAQ
What are the 7 types of RAG in order of complexity?
Simple, Context-Aware, Multi-Hop, Hybrid, Adaptive, Agentic, Self-Improving. But complexity ≠ value. Simple RAG with good chunking beats Agentic RAG with bad data.
Which RAG type gives the best accuracy?
Depends on your data and queries. In our benchmarks at SIVARO, Agentic RAG scored highest on complex queries (89% accuracy) but lowest on simple ones (76% — overhead hurt). Hybrid RAG was most consistent across query types.
Can you combine multiple RAG types?
Yes. Most production systems do. We run Hybrid + Context-Aware + Self-Improving for a manufacturing client. The types aren't mutually exclusive.
What's the most common mistake when implementing RAG?
Ignoring data quality. I've seen teams spend months tuning retrieval strategies for garbage data. Clean your data first. Test your chunks. If the retrieval quality is below 80%, fix data before touching code.
Does RAG eliminate hallucinations?
No. It reduces them. In our testing, good RAG drops hallucination rates from ~20% to ~3-5%. It doesn't eliminate them. You still need guardrails, validation, and human oversight.
Which RAG type is best for production?
Hybrid RAG with Context-Aware chunking and Self-Improving feedback loops. That combination handles 90% of production use cases. Add Multi-Hop or Agentic only when queries demand it.
How do I evaluate which RAG type I need?
Run 100 real queries against Simple RAG. Measure accuracy. If it's below 80%, try Context-Aware. Still below? Try Hybrid. Only add complexity when baseline approaches fail.
What I'd Do Differently
If I could go back to early 2023, I'd tell myself: stop chasing the perfect retrieval algorithm. Start with clean data. Add Context-Aware chunking. Measure everything. Only then think about advanced types.
The 7 types of RAG aren't a hierarchy. They're a toolkit. Simple screwdriver for simple problems. Agentic chainsaw for complex ones. Use the right tool, not the most impressive one.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.