Agentic RAG
Agentic RAG goes beyond basic retrieve-and-generate. The agent analyzes each query to decide whether retrieval is even needed, retrieves strategically, and assesses response quality, making the RAG pipeline intelligent rather than mechanical.
Agentic RAG pipeline
The full pipeline from query analysis through routing to generation
Basic RAG retrieves documents and passes them to an LLM in one shot. Agentic RAG adds decision-making: the agent can choose which retrieval strategy to use, decide if results are good enough, and iterate if needed. It turns a passive pipeline into an active problem-solver.
Agentic RAG decision flow
class AgenticRAG:
def __init__(self):
self.document_store = DocumentStore()
self.llm = get_llm()
def analyze_query(self, query):
"""Determine if retrieval is needed."""
prompt = f"""Analyze this query:
Query: {query}
Does this need document retrieval? (yes/no)
What is your confidence? (0-1)
"""
return self.llm.generate(prompt).content
def process_query(self, query):
analysis = self.analyze_query(query)
needs_retrieval = "yes" in analysis.lower()
if needs_retrieval:
docs = self.document_store.search(query)
context = "\n".join([d["content"] for d in docs])
response = self.generate_with_context(query, context)
else:
response = self.generate_without_context(query)
quality = self.assess_response_quality(query, response)
return {"answer": response, "quality": quality}
def assess_response_quality(self, query, response):
prompt = f"""Rate accuracy, completeness, helpfulness (1-10):
Query: {query}
Response: {response}"""
return self.llm.generate(prompt).contentAgenticRAG analyzes queries, decides retrieval strategy, and assesses quality.
AI prompt: Try it with AI
Loading practice…
Quiz: Quiz
Loading practice…
Matching exercise: Match agentic RAG concepts
Loading practice…
Flashcards: Flashcards
Loading practice…
Agentic RAG transforms passive retrieval into intelligent decision-making. Next, we learn how to orchestrate complex multi-step workflows with dependencies.