Between 2024 and 2025, new AI concepts have been arriving at a relentless pace. ChatGPT, Claude, DeepSeek, Agent, MCP, Vibe Coding — these names show up everywhere in tech discussions. A lot of people I know want to understand what each one means and how they relate to each other, but the information overload makes it hard to get a clear picture. This article aims to build a mental map that connects the core concepts — LLM, Agent, Skills, MCP — and shows how they fit together. Once you have this framework, any new concept becomes easier to place, and you’ll be in a better position to pick the right tool for your problem.
What an LLM Actually Is
An LLM (Large Language Model) is the foundation of every AI system in this landscape. Trained on massive text corpora, it learns human language patterns and knowledge, accepting natural language input and producing text output. Understanding LLMs requires clearing up three common misconceptions. An LLM isn’t a search engine — a search engine retrieves existing answers from an index, while an LLM generates the most probable answer based on its training data. This mechanism is what produces hallucinations: output that looks plausible but is actually wrong. An LLM is offline by default — its knowledge has a hard cutoff date (GPT-4’s knowledge stops in early 2024), and without additional configuration, it can’t access information from after training. An LLM doesn’t truly understand anything — it operates through probabilistic prediction, inferring the most likely next token from preceding context. This is large-scale pattern matching, not comprehension in any human sense.
Models vs. Products
Model and product are two concepts that get conflated constantly. A model (like GPT-4 or Claude 3.5) is the underlying language processing engine. A product (like ChatGPT or Claude’s web interface) is the polished, user-facing application built around it, complete with UI, feature logic, and safety policies. The same model can power multiple products — GPT-4 runs both ChatGPT and Microsoft Copilot. Comparing GPT-4 to ChatGPT is like comparing an engine to a car. They exist on entirely different levels.
Choosing a Mainstream LLM
| Model | Provider | Strengths | Best For |
|---|---|---|---|
| GPT-4/GPT-4o | OpenAI | Strong general capabilities, mature ecosystem | Creative writing, general Q&A |
| Claude 3.5/4 | Anthropic | 200K token context, rigorous reasoning | Long document analysis, code review |
| DeepSeek-V3/R1 | DeepSeek | Open-source, cost-effective, strong at math | Technical research, budget-sensitive work |
| Gemini 1.5/2.0 | Natively multimodal, Google ecosystem | Multimedia processing | |
| Qwen2.5 | Alibaba | Chinese-optimized, open-source friendly | Chinese-language scenarios |
Casual users can start with ChatGPT or Claude. Those on a budget or preferring open-source should try DeepSeek. For Chinese-language work, Kimi or Qwen are solid options. Context length is a parameter that’s easy to overlook but critically important — it directly determines how much information the model can process. Kimi’s 2 million character context is roughly equivalent to dozens of books. Claude’s 200K tokens translates to about 300,000 Chinese characters. If you’re working with long research papers or legal contracts, you need a model with a large context window, or it will lose earlier information as it processes later content.
Agents: AI That Acts on Its Own
If you’ve only used ChatGPT’s web interface, you’ve experienced pure conversational LLM mode. An Agent is a class of AI system that can autonomously plan, call tools, and complete end-to-end tasks. An LLM is like a chatty encyclopedia — you ask, it answers, but it takes no action. An Agent is more like an executor that solves problems independently. Tell it to book a flight to Shanghai for tomorrow, and it’ll search flights, compare prices, fill in details, and complete the entire booking flow.
The core differences show up across four dimensions. Interaction pattern: a conversational LLM works in question-answer pairs; an Agent can execute multiple steps autonomously. Tool usage: a conversational LLM depends on whether the product layer exposes tool interfaces; an Agent’s architecture is designed around calling external tools. Task completion: a conversational LLM gives advice and generates content; an Agent actually performs operations and delivers results. Memory: a conversational LLM is limited to single-turn or short multi-turn context; an Agent can maintain state and memory across sessions.
The ReAct Paradigm
An Agent’s workflow follows the ReAct paradigm (Reasoning + Acting), built around a loop: observe the environment, understand the goal, reason about the next step, select a tool, execute the action, observe the result, then iterate until the task completes or a termination condition triggers. Take a concrete example: ask an Agent to analyze a financial report and generate charts. It would first use file operations to read the document, then invoke code execution to run Python for data analysis, then call a visualization tool to generate charts, then check whether the output meets expectations, and if not, adjust parameters and re-execute.
Mainstream Agent Products
| Product | Type | Core Capability |
|---|---|---|
| Claude Code | Coding Agent | Direct codebase manipulation, batch refactoring, debugging |
| Manus | General Agent | End-to-end task automation, can operate user interfaces |
| AutoGPT | Experimental Agent | Autonomous task decomposition, iterative execution |
Cursor and GitHub Copilot aren’t agents in the strict sense. They’re better described as augmented editors — their core value lies in assisting coding, not in autonomous planning and multi-step task execution. A real Agent can independently break down goals, choose execution paths, orchestrate tools, and deliver results.
Skills: The Agent’s Capabilities
Skills are the specific capability modules an Agent can invoke. Without Skills, an Agent would be confined to the reasoning layer, unable to interact with the real world. Common Skills span several domains: file operations (read/write/search local files), web search (accessing real-time information), code execution (running Python, Bash, etc.), database queries (SQL operations), and message sending (email, IM notifications). The breadth of available Skills directly determines the Agent’s capability boundary. The model sets the AI’s intelligence ceiling, the Agent sets the AI’s action floor, and Skills are the concrete vehicle for that action capability.
MCP: A Standard Interface for AI
MCP (Model Context Protocol) is an open protocol released by Anthropic in November 2024, aimed at standardizing how AI connects to external data sources. Think of MCP as the USB-C standard for AI. Previously, every AI application needed a custom adapter to connect to each new database or tool. MCP lets a developer implement the interface once, and any MCP-compatible AI application can use it.
MCP’s architecture has three components. The Host is the application running the AI (e.g., Claude Code). The Client maintains the connection between Host and Server. The Server provides specific tool capabilities (file system access, GitHub integration, database queries, etc.). Official Server implementations already exist for file systems, GitHub, PostgreSQL, and more. Through MCP, an AI can directly access a user’s files, code repositories, databases, and collaboration tools. MCP is the core infrastructure for Agent capabilities and is becoming the de facto standard for AI-to-external-world connectivity.
Vibe Coding: Building Software in Natural Language
Vibe Coding is a concept proposed by former Tesla AI Director Andrej Karpathy in early 2025. It describes a development style where you describe requirements in natural language and the AI generates the code. It represents a shift: the developer’s focus moves from writing code to describing requirements, from managing syntax details to expressing intent, from manual implementation to review and iteration. A typical scenario: you describe wanting a to-do web app with task addition, completion marking, and deletion, with a clean, attractive interface — and the AI generates complete HTML/CSS/JavaScript code. The developer just reviews the output and makes minor adjustments.
Vibe Coding has clear boundaries. It works well for prototyping, simple applications, and scripting tasks. Complex system architecture still requires professional developers for design decisions and performance tuning. The irony is that this approach demands even stronger requirements articulation skills. If your requirements are vague or incomplete, the generated code will miss the mark.
The Product Landscape
The AI product ecosystem breaks down into three broad categories by use case.
Conversational products center on the LLM, providing chat interfaces. ChatGPT runs on GPT-4o/o3 with a rich ecosystem and many plugins. Claude runs on Claude 3.5/4, offering 200K token context and rigorous reasoning. Kimi runs on Moonshot’s proprietary model with a 2 million character context, excelling at long document processing. DeepSeek runs on DeepSeek-V3/R1, open-source and transparent, with standout math reasoning and low pricing.
AI coding assistants target development workflows. Cursor positions itself as an AI IDE, covering code generation, explanation, and refactoring for daily development. GitHub Copilot focuses on code completion, providing real-time suggestions as you type. Claude Code is a coding Agent designed for complex tasks, supporting batch refactoring and multi-file operations. For everyday coding, Cursor or Copilot is enough. When you need large-scale refactoring or full-project architectural understanding, Claude Code pulls ahead.
Content generation tools cover visual and multimedia domains. Midjourney leads in artistic style for image generation and operates through Discord. DALL·E 3 integrates deeply with ChatGPT for low-friction usage. Sora is OpenAI’s video generation tool, currently available only to select users. Nano Banana is Google’s experimental image-text processing tool supporting mixed image-text tasks, still iterating on its feature set.
A Decision Framework
Choose tools based on your specific needs:
| Scenario | Recommended Tool | Why |
|---|---|---|
| Daily Q&A, brainstorming | ChatGPT / Claude | Versatile, mature interaction |
| Reading papers / long docs / legal files | Kimi / Claude | Large context windows handle long text |
| Writing or reviewing code | Claude Code / Cursor | Strong code comprehension and manipulation |
| Illustrations, design mockups | Midjourney / DALL·E 3 | Midjourney for art style, DALL·E for convenience |
| Video content | Runway / Pika / Sora | Sora not yet broadly available |
| Sensitive data handling | Local model + OpenClaw | Data stays on your machine, privacy-controlled |
| Math / logic reasoning | DeepSeek-R1 / Claude / o3 | DeepSeek-R1 is open-source with strong reasoning |
| Office task automation | Manus / AutoGPT | End-to-end task automation |
Choose your starting point based on your background:
| User Type | Starting Point | Next Step |
|---|---|---|
| Complete beginner | ChatGPT web | Claude, Kimi |
| Office worker | Kimi for docs + ChatGPT for writing | Learn Prompt engineering |
| Creator | Midjourney + Claude | Learn AI workflows |
| Programmer | Cursor / Claude Code | MCP + Agent development |
| Power user / tinkerer | Local model + OpenClaw | Build your own AI workflow |
Common Misconceptions and Practical Advice
Several misconceptions deserve correction when using AI. AI doesn’t know everything — LLMs have knowledge cutoff dates and can’t access new information without internet access. AI makes mistakes — it hallucinates, delivering incorrect information with convincing confidence. AI doesn’t truly understand — its output is based on probabilistic pattern matching. Expensive models aren’t always the right choice — for simple tasks, smaller models like GPT-4o-mini respond faster and cost less. Current AI augments humans; it replaces repetitive work, not human judgment entirely. Good prompts are about expressing requirements clearly, not piling on fancy wording.
A few practical recommendations. Cross-verify important information across multiple AI tools or search engines — don’t blindly trust a single source. Give the AI sufficient background and context; input quality directly determines output quality. If the first result isn’t satisfactory, keep iterating and refining your requirements — iteration is the normal path to high-quality output. Understand what AI is good at and what it isn’t. Creative generation, information organization, and first drafts are good fits for AI. Critical decisions, medical advice, and legal guidance still need professional judgment. AI output is non-deterministic, so save important results promptly.
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