Recommended[1] | Source[2]
Supplementary reading to the research paper

RAGate: A Gating Model

  • RAGate models conversation context and relevant inputs to predict if a conversational system requires RAG for improved responses.
  • Conclusion: Effective application of RAGate in RAG-based conversational systems identifies when to use appropriate RAG for high-quality responses with high generation confidence.

Validation of RAGate

  • Experimentation: Conducted on KETOD (an enriched Task-Oriented Dialogue dataset based on SGD) spanning 16 domains.
  • Findings: Without external knowledge, early-stage conversations were more natural and diverse—showing that misapplied knowledge can harm experience.

🧠 Understanding Conversational System Evolution

Definition

Integrating LLMs into conversational systems means using pre-trained models to power response generation, dialogue flow, and intent interpretation.

Capabilities of LLMs
  • Context understanding
  • Coherent response generation
  • Basic reasoning over input sequences
Traditional Conversational Systems
  • Rule-based systems (rigid, manual)
  • Template-based responses (non-generative)
  • State-machine driven dialogue flows (cumbersome)
  • Simple ML models (Naive Bayes, SVMs)
  • Information-retrieval based (non-generative, high dependency on database)
Advantages of Traditional Systems
  • Cost efficient
  • Deterministic behavior (ideal for critical use-cases)
  • Low-latency, especially for embedded or constrained environments
Example Use Cases Traditional Systems

📡 RAG in Conversational Systems

Why RAG?
  • Augments LLMs with real-time knowledge retrieval
  • Grounds generation with contextual facts
  • Improves trust, accuracy, and adaptability
Types of RAG
  • Single-pass RAG
  • Iterative RAG
  • Knowledge-Enhanced RAG
  • Hybrid RAG
  • Memory-Augmented RAG
  • Cross-Attention RAG
  • Modular RAG
  • Task-Specific RAG

⚙️ Efficiency Enhancements & RAG Infrastructure

Dense Passage Retrieval

Uses dense embeddings for better semantic matching vs. sparse approaches (e.g., TF-IDF).

Public Search Services
  • Elasticsearch
  • Google Cloud Search API
Task-Oriented Dialogue (TOD) Systems

Models aiming to maximize likelihood over multi-domain task data.

SURGE (Subgraph RAG)

Combines contrastive learning with subgraph retrieval to improve grounding.

[1] Elvis from X | [2] Adaptive Retrieval-Augmented Generation for Conversational Systems