AI - Comparing LLM , RAG , AI Agent , Agentic AI
1. LLM (Large Language Model)
Context-Free Generation: Produces text purely from prompt input without external retrieval.
Fast & Simple: Easy to deploy with low complexity, but limited in context understanding or integrating new data sources.
Stages : Begins with next-word prediction on static training data—ideal for simple text generation and chatbots with limited context
Usage : For generating text or answering simple, general questions without needing external data.
2. RAG (Retrieval-Augmented Generation)
Knowledge-Enhanced: Combines LLM output with real-time retrieval from external sources for more accurate, up-to-date responses.
Data-Dependent Precision: Excels at Q&A and knowledge tasks but is sensitive to the quality and structure of underlying data sources.
Stages : Enhances LLMs by retrieving real-time external knowledge, grounding responses with accurate, up-to-date information.
Usage : For retrieving and summarizing up-to-date, domain-specific knowledge during a conversation.
3. AI Agent
Autonomous Task Execution: Uses planning, reasoning, memory, and tool integrations to complete workflows that need decision-making.
Goal-Oriented Automation: Ideal for well-defined tasks like multi-step data processing or tool-based operations needing structured plans.
Stage : Introduces planning, memory, and tool use to autonomously execute multi-step workflows with reasoning.
Usage : For automating single-user tasks that need planning and tool use—like research assistance.
4. Agentic AI
Multi-Agent Collaboration: Deploys multiple specialized agents that coordinate, divide labor, and even negotiate to handle complex problems.
Adaptive & Persistent: Supports memory, feedback, and reasoning across agents to tackle large-scale tasks requiring ongoing strategy.
Stage : Evolves into a collaborative multi-agent ecosystem where specialized agents coordinate, share memory, and divide tasks to solve complex problems together.
Usage : For managing complex, multi-step, multi-user processes where multiple specialized agents coordinate as an ecosystem.