Subject Agnostic Problem Routing And Classification

via “routing pattern for dynamic task direction based on query classification”

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

Unique: Implements routing as an intelligent classification step that analyzes query characteristics to select specialized handlers, rather than using static rules or random assignment, enabling adaptive pipeline selection based on query semantics.

vs others: More efficient than single-pipeline systems by avoiding unnecessary processing steps, and more adaptive than rule-based routing by using LLM reasoning to classify queries based on semantic content.

via “multi-subject-problem-classification-and-routing”

Unique: Lightweight, mobile-optimized classification layer that routes to specialized solvers rather than using a single monolithic LLM, enabling subject-specific accuracy and faster inference on resource-constrained mobile devices

vs others: More efficient than asking a general-purpose LLM to solve all problem types because specialized solvers for each domain are faster and more accurate, while the routing layer adds minimal latency compared to the cost of a single large model inference

via “issue-classification-and-routing”

via “user inquiry classification and routing”