Agentic Adaptive RAG

Project Snapshot

  • Category: Generative AI / Agentic RAG Systems
  • Focus: Adaptive retrieval, hallucination mitigation, reliability
  • Architecture: Agentic, state-driven, self-correcting
  • GitHub: Agentic Adaptive RAG

Executive Summary

Agentic Adaptive RAG is an end-to-end, production-grade Retrieval-Augmented Generation system designed to deliver reliable, context-aware answers by dynamically adapting its retrieval and reasoning strategy. Unlike traditional RAG pipelines, the system evaluates its own inputs and outputs, routing queries, validating evidence, and correcting itself at runtime.

Problem Statement

Conventional RAG systems follow a fixed retrieve-then-generate flow, which often leads to:

  • Hallucinated answers due to weak or irrelevant retrieval
  • Inability to adapt when local knowledge is insufficient
  • Lack of quality control over generated responses
  • Poor reliability in production and enterprise use cases

Solution Overview

I designed Agentic Adaptive RAG as a state-driven, agentic system that dynamically decides how to answer a query instead of assuming a single retrieval strategy.

  • LLM-based query routing between vector search and web search
  • Document relevance grading before generation
  • Hallucination detection and answer validation
  • Self-correcting control flow using conditional execution paths

Architecture & Approach

  • Implemented as a stateful agent graph using LangGraph
  • Each node performs a single responsibility (retrieve, grade, generate)
  • Centralized state object enables adaptive decision-making
  • Fallback strategies trigger web search or regeneration automatically
  • Designed for extensibility, testing, and production deployment

Key Capabilities

• Adaptive Query Routing

Dynamically routes queries between vector store retrieval and live web search based on intent and domain relevance.

• Retrieval Quality Grading

Uses LLM-based relevance scoring to filter low-quality or off-topic documents before generation.

• Hallucination Detection

Verifies that generated answers are grounded in retrieved evidence, triggering retries when unsupported.

• Self-Correcting Workflows

Automatically adapts execution paths using conditional logic, enabling reliable, production-ready behavior.

Impact & Outcomes

  • Significantly reduced hallucinations in generated responses
  • Improved answer relevance and factual grounding
  • Enabled adaptive reasoning instead of fixed RAG pipelines
  • Designed a reusable blueprint for enterprise-grade AI assistants

Tech Stack

Languages: Python
LLM Frameworks: LangChain, LangGraph
Models: Google Gemini (LLM & embeddings)
Vector Store: Chroma
Search: Tavily Web Search
Testing: Pytest, structured output validation