Are you only using AI as a chat tool or a more intelligent browser? Do you still struggle to understand what AI large models, intelligent agents, and AI workflows really are? This guide will break down the underlying logic of the AI application layer in the most accessible way, even for complete AI beginners.
The 3-Layer Pyramid of AI Applications
To avoid confusion, let's structure all AI products into a 3-layer pyramid:
- Core Brain (Large Language Models - LLM)
- Senses & Limbs (Multimodal Models)
- Skeleton & Tools (Internet, Knowledge Bases, Workflows, Intelligent Agents)
These three layers determine what AI can actually do for you.
Layer 1: The Core Brain — Large Language Models (LLM)
LLMs are the "brain" of AI. Think of them as a professor who knows everything — they can answer any question quickly and have a perfect memory. Popular LLMs include DeepSeek, Doubao, Qwen 3, ChatGPT, and Gemini 3.0.
Key Capabilities
- Process text and logical thinking
- Act as a central hub for understanding and generating information
Critical Limitations
- Knowledge Limitations: Time lag — an LLM trained last month won't know what happened this month. Scope — LLMs don't know your personal or company-specific data. Ask Doubao about your next week's work plan, and it will make things up because that data isn't in its training set.
- Passive Nature: LLMs only respond when asked. They won't proactively provide information unless prompted.
Mastering Prompts
The key to working with LLMs is writing good prompts. A prompt's essence is: Don't let the model guess who you are. Be specific about context, goals, and constraints.
You are a career coach. I'm a 28-year-old marketing
specialist with 5 years of experience in digital marketing.
I want to transition into product management. My strengths
are data analysis and user research. Please create a 1-year
career transition plan, including skills to learn and job
search strategies.Layer 2: Senses & Limbs — Multimodal Models
Multimodal models are AI's "senses and limbs" — they let AI see, hear, speak, and create. Think of them as giving the LLM "a painter's hands, a director's eyes, and a singer's voice."
- Generate images (Nano Banana Pro, Midjourney), videos (Veo 3.1, Sora 2), audio (IndexTTS, Suno), and digital humans (HeyGen)
- Enable AI to interact with text, images, audio, and video
Limitation: Multimodal models still inherit the LLM's flaws — no real-time data, no personal/company information access, and they're still passive until instructed.
Layer 3: Skeleton & Tools
This layer adds "skeleton and tools" to AI, solving its limitations in real-time data, personalization, and proactivity.
1. Internet Search Tools
To fix the LLM's time lag, we give it internet access. When you ask Doubao "What's the news today?" it runs a web search — like "plugging the professor into the internet."
2. Knowledge Bases
To add personal/company data, use knowledge bases (NotebookLM, IMA). Upload a contract and ask:
Analyze this contract for potential risks, specifically
focusing on payment terms and liability clauses.The LLM will answer based on your actual data, not made-up information.
3. Workflows (Standard Operating Procedures)
Workflows turn AI into a "factory line worker" — they enforce consistent steps to ensure stability. Example: Creating a Viral Article Workflow:
- Search for hot topics on Toutiao in a specific niche
- Extract the title style of the top 3 articles
- Imitate that style to write an article that fits your personal brand
This turns "unpredictable creativity" into a "reliable assembly line."
4. Intelligent Agents
Intelligent agents are like "project managers" — they think independently, plan, and act. Unlike workflows (which follow fixed steps), agents decide which tools to use and which workflows to run.
Example: Tell an agent "Help me plan a trip to the Moon." It will: check Moon travel tickets, gather travel guides, compile everything into a document, and send it to you. Agents turn AI from a "chatbot" into a "doer."
Building AI Solutions with Kouzi (扣子)
Kouzi (Coze) is ByteDance's platform that lets you assemble all these layers into a working AI solution — no coding required. Think of it as a "super factory" for building intelligent agents.
- Choose a Brain (LLM): Pick an LLM like Doubao or ChatGPT
- Add Tools & Senses: Integrate internet search, knowledge bases, image/audio/video generators
- Create Workflows (SOPs): Define the steps AI should follow
- Build an Intelligent Agent: Package everything into an agent that delivers results
Final Takeaway
AI applications work in a 3-layer pyramid: Brain (LLM) processes text and logic, Senses/Limbs (Multimodal) adds visual/audio capabilities, Skeleton/Tools adds real-time data, personalization, and proactivity. With tools like Kouzi, you can build practical AI solutions even if you don't know how to code.
常见问题
What's the difference between a workflow and an intelligent agent?
A workflow follows a fixed, pre-defined sequence of steps — like a factory assembly line. It's predictable and reliable, but inflexible. An intelligent agent decides which steps to take based on the situation — like a project manager who chooses tools and workflows dynamically. Workflows are best for repetitive, well-understood tasks (publishing daily reports, processing standard forms). Agents are best for open-ended tasks where the path isn't known in advance (researching a new topic, planning an event). Kouzi lets you build both and even combine them: use workflows for stable sub-tasks and an agent to orchestrate which workflows to run.
Do I need coding skills to build AI agents with Kouzi?
No — that's Kouzi's main value proposition. The platform uses a visual, drag-and-drop interface. You select an LLM from a dropdown, add tools by toggling switches, and create workflows by connecting nodes visually. For most common use cases (chatbots, customer service, content automation), you can build a working agent in under an hour with zero code. That said, for advanced customizations or integrating proprietary APIs, some technical knowledge helps — but it's optional for getting started.
How do knowledge bases actually work with LLMs?
Knowledge bases use a technique called RAG (Retrieval-Augmented Generation). When you upload a document, the system splits it into chunks, converts each chunk into a vector embedding, and stores them in a vector database. When you ask a question, the system finds the most relevant chunks and injects them into the LLM's prompt as context. This way, the LLM "reads" your documents in real-time and answers based on them, rather than relying on its training data. This is how NotebookLM can answer questions about a contract you just uploaded, even though that contract was never in the model's training data.