Unlock the core passcode in the AI era - Why do you have to understand RAG, Agent, and MCP?
In the AI era, how can you make big models understand you better? Revealing the three key technologies of RAG, Agent, and MCP to make your AI smarter!
Core content:
1. The three major shortcomings of AI big models: illusion creation, paper talk, and data silos
2. RAG, Agent, and MCP: three major technologies to help improve AI capabilities
3. How the three musketeers work together to improve the practicality and intelligence of AI
In the context of the AI era, AI big models have shown significant advantages in many application scenarios with their powerful language understanding and generation capabilities, presenting an appearance of being omniscient. However, after in-depth analysis, it is not difficult to find that the current AI big models seem to be omniscient, but in fact they face three fatal shortcomings :
Illusion maker : Without real-time knowledge base support, it is easy to fabricate wrong information → Example: Ask ChatGPT "the latest medical insurance policy in 2024", it may make up terms
Armchair strategists : can analyze problems but cannot perform specific operations → Example: AI can recommend travel routes but cannot automatically book flights and hotels
Data silos : Difficulty connecting to real-world systems such as enterprise databases and IoT devices → Example: Medical AI cannot directly access patient medical records
RAG, Agent, and MCP are like three brave "breakthroughs" and are known as the "AI landing triangle" in the industry. They contribute to completing the puzzle of AI capabilities:
1. Knowledge Courier RAG: Accurately delivering information
(1) Professional definition
Retrieval-enhanced generation technology operates through a dual-engine approach of knowledge base retrieval and large model generation . It first uses a vector database to match documents related to the question, and then generates a well-founded answer, reducing the probability of AI "talking nonsense".
(2) Human language translation
AI's reference secretary is like looking up information to write a paper: first look through the book to find data (retrieval), then organize it into a report (generation). RAG ensures that every answer from AI has a "reference"!
3-word memorization core : check-find-edit
2. Intelligent Commander Agent: Autonomous Disassembly Tasks
(1) Professional definition
The autonomous decision-making system has the closed-loop capabilities of perception (receiving instructions), decision-making (breaking down steps), and execution (calling tools), and can plan complex task processes like humans.
(2) Human language translation
Your 24-hour AI butler, for example, if you say "arrange a birthday party", it will automatically: order a cake → check the weather and choose a venue → send group invitations → remind you to purchase, without you having to intervene in the entire process!
The core of the 3-word memorization : Listen - Think - Do
3. Universal Operator MCP: Opening up the Ren and Du Meridians
(1) Professional definition
The model context protocol establishes a standard communication channel between AI and external systems, allowing the intelligent body to call API/database/hardware devices in a standardized manner, breaking through the limitations of "closed-door development".
(2) Human language translation
The USB hub in the AI world is like using an adapter to connect a USB flash drive, projector, and mobile hard drive at the same time. MCP allows AI to easily connect to various tools such as WeChat payment, map navigation, smart home, etc.
The core of the three-word memorization : connect-pass-control
4. How do the Three Musketeers team up to fight monsters?
(1) Professional collaborative chain
(2) Human language version
Suppose you want to plan a self-driving tour :
RAG : Quickly check route guides and scenic spot reviews (check information)
Agent : Plan your daily schedule and balance driving time with play time (making plans)
MCP : automatic car rental, synchronized navigation to the vehicle system, ticket booking (on-site)
5. Real Case: AI Health Manager
(1) Morning scene
You ask: "What should I do if I always have headaches recently?"
RAG Action : Search medical guidelines, similar cases, and drug instructions
Agent decision : suggest measuring body temperature first → record symptom frequency → recommend over-the-counter medicine
MCP linkage :
Wake up the smart bracelet to detect heart rate
Connect to pharmacy API to compare prices and deliver medicines
Synchronize your schedule to add medical reminders
(2) Technical analysis
Knowledge layer (RAG) : medical database/literature library/drug library
Decision-making layer (Agent) : Symptom analysis engine/medication logic tree
Connection layer (MCP) : wristband Bluetooth protocol/drugstore API/calendar interface
6. A Beginner's Guide to Shorthand
Build a cognitive framework in 3 steps : 1️⃣First recognize the roles : RAG=Librarian/Agent=CEO/MCP=Diplomat 2️⃣Then remember the functions : look up information → make a plan → execute 3️⃣Finally look at the combination : any AI application is a combination of these three modules!
