Adaptive RAG: The Smart, Self-Correcting Framework for Complex AI Queries

Author(s): Sai Bhargav Rallapalli

Originally published on Towards AI.

Introduction: Why Adaptive RAG is a Game-Changer for AI Retrieval

When you ask your AI assistant a question, have you ever wondered how it decides whether to answer quickly from its memory or deep-dive into a knowledge base?

This isn’t magic. It’s Adaptive Retrieval-Augmented Generation (Adaptive RAG) — the next-gen framework that balances speed, accuracy, and smart decision-making in AI retrieval workflows.

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Adaptive RAG is smarter. It first asks:

Is this a simple question I can answer directly?Should I search the web?Should I dig into the internal database?

Analogy:

Adaptive RAG is like a personal assistant who decides whether to answer you on the spot, call a friend, or check the company’s archives.

Query Classification: The system identifies if the question is simple, medium, or complex.Routing Decision: It selects the fastest, most relevant data source (memory, web, vector DB).Self-Correction: If the retrieved info is poor, Adaptive RAG rewrites the query or switches to a fallback source.Hallucination Check: Ensures the AI isn’t making up answers.image from langchain documenttion for adaptive ragmkdir adaptiveRAGcd adaptiveRAGuv inituv venvsource .venv/bin/activateuv add -r requirements.txtimport osfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain_community.document_loaders import WebBaseLoaderfrom langchain_community.vectorstores import FAISSfrom langchain_openai import OpenAIEmbeddingsfrom langchain_core.prompts import ChatPromptTemplatefrom langchain_openai import ChatOpenAIfrom langchain import hubfrom langchain_core.output_parsers import StrOutputParserfrom langchain_community.tools.tavily_search import TavilySearchResultsfrom typing import List, Literalfrom typing_extensions… Read the full blog for free on Medium.

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Published via Towards AI