Clarify the underlying logic, so even beginners can understand the core fundamental concepts of AI!

Written by: TinTinLand

The rapid development of AI technology is no longer a celebration for niche enthusiasts; it has become a wave of productivity revolution that is entering every household.

Do you remember a few months ago, under the Tencent Building in Shenzhen, hundreds of users were holding computers just to wait for a chance to deploy OpenClaw? When the “Little Dragon Shrimp” went viral and spread everywhere, whether it was professionals using it to automatically process reports and write code, or companies using it to build autonomous intelligent assistants, AI had already fully permeated every corner of work and everyday life. At the same time, various AIGC applications are accelerating in adoption—from AI painting and intelligent customer service to enterprise-level agent deployments—its traces have already spread throughout daily life.

According to data from relevant authorities, the global AI market is expected to exceed $900 billion by 2026, China’s AI core industry scale will reach 1.2 trillion yuan, 88% of companies say AI helps increase annual revenue, and 76% of large enterprises have deployed AI-related applications; and as OpenClaw drives an upgrade of the AI Agent paradigm, global Token consumption has increased by more than 4 times within a single month. By the end of 2026, global monthly Token consumption is expected to see exponential growth. AI is shifting comprehensively from conversation tools to productivity engines, profoundly changing companies’ cost structures and individuals’ work patterns.

However, behind the rapid growth in data, many users only scratch the surface of AI. When facing high-frequency keywords such as Prompt, Token, and RAG, they either look completely lost or only understand part of the picture, making it difficult to realize AI’s full value.

We interact with AI every day, yet we are often left baffled by a pile of professional terms. For example, when using OpenClaw, if you don’t understand Context Window, you can’t efficiently complete multi-step tasks by leveraging its persistent memory capability. If you don’t know Plugin, you won’t know how to extend its functions to fit your own needs. When generating AI copy, if you don’t understand Prompt engineering, you can’t write precise instructions. So instead of blindly following trends and using AI tools, it’s better to take the initiative and master the core concepts of AI technology to seize the first-mover advantage in the wave of artificial intelligence. TinTinLand has prepared a “Core AI Basics: Easy for Beginners to Understand” knowledge-sharing session for you—so once you finish it, you’ll be able to grasp the complete logic of how AI works, and you’ll never be thrown off by jargon again!

Basic Layer — The Foundation of AI Technology

The basic layer is the foundation of AI. Like the foundation and building materials for a house, it directly determines the technical heights AI can reach, and it is the starting point of all AI applications.

LLM: Large Language Model, AI’s Super Brain

Many people think that large models like ChatGPT are all there is to AI. In reality, that understanding is only half correct. The foundation of AI applications is the LLM (Large Language Model, large language model). It is a natural language processing system built on deep learning, whose core is to learn the grammar, semantics, and logic of human language through autonomous training on massive amounts of text data during pretraining. In the end, it has comprehensive capabilities to understand context, generate text that fits the situation, and complete complex language tasks. This is the “core brain” of all generative AI.

In simple terms, AI writing tools use LLMs to generate logically consistent text, and code generation tools use LLMs to understand programming syntax and requirements. In just 2025, the number of enterprise-level LLM deployments increased by 187% year over year, covering all industries such as finance, healthcare, and education. In real-world practice, users generally don’t need to build an LLM themselves; they can call mature models directly. Enterprise applications can fine-tune open-source LLMs to adapt to their own business scenarios.

AIGC: Generative AI, a Creativity Engine

AIGC (AI Generated Content, generative AI) refers to intelligent technology that uses AI techniques to automatically generate content such as text, images, audio, video, and code. It differs from the inherent limitations of traditional AI—“it can only analyze, not create.” This is the key shift that moves AI from tools to creation. Users open the dialogue command box, input the relevant text prompts and reference materials for their needs, and the large AI model generates the corresponding text and video content after parsing the requirements. Through manual fine-tuning, it produces finished, high-quality works.

Currently hot AIGC generation software applications/websites include MidJourney, Stable Diffusion, Runway, and more. The proportion of investment in human labor is reduced by about 30%, while content generation efficiency increases by 5-10 times compared with manual work—fully unlocking application potential and product coverage in design and cultural-creative industries.

Interaction Layer — Letting Humans Effectively Command AI

The AI in the basic layer is powerful, but it needs to be translated into human needs through the interaction layer so that AI can understand and do what you want. This directly determines our communication efficiency and outcomes with AI.

Prompt: Prompt Words, Understanding AI Instruction Guidance

Prompt (prompt words) are the various detailed instructions that humans input to AI, including requirement descriptions, scenario constraints, format requirements, and more. The purpose is to make AI clearly understand the task objectives and generate results that match expectations. When users present various requirements to AI, the instructions they input are called prompts. High-quality prompts allow AI to output content that is more accurate and aligned with the user’s established expectations.

Common prompt structure elements include — role setting (Role), available tools (Tools), task goals (Goal), output format (Output Format), rules and steps (Rules&Steps), and examples (Example). In real AI dialogue practice, there are almost no prompts that are perfect from the start. You usually need to do a quick pre-run to see the results and adjust the instructions based on the actual situation, until reaching the ideal prompt editing state.

Token: Word Tokens, Grasping AI’s Smallest Understanding Unit

In real-world AI application scenarios, Token (word token) is the smallest semantic unit of text. It is the “atom” that AI uses to understand and process language. This is mainly because AI cannot directly recognize complete sentences or words; instead, it splits the text into individual Tokens and then performs computation and understanding. As authentication tokens for identity verification, Tokens can be used in scenarios such as API access control.

As the core measurement unit for AI computational cost, China’s average daily Token consumption has surged from about 100 billion at the start of 2024 to breaking 30 trillion by the end of June 2025. This figure intuitively reflects the adoption speed of AI applications. We believe that in the future, data centers will no longer be just storage warehouses; they will become intelligent factories that produce Tokens.

Context Window: Context Window, AI’s Short-term Memory

Context Window (context window) directly affects long-text processing and multi-turn dialogue experiences. For example, when processing an article with 5,000 words (about 3,000 Tokens), if the model’s context window is only 2,048 Tokens, the large AI model will exhibit a “fragmentation” effect and cannot understand the latter half of the article. Therefore, only when the Context Window is long enough to accommodate a sufficient amount of information can longer content be processed continuously; otherwise, you will get the situation of “forgetting old information.”

Currently, when we need to process long texts, we can choose large-context-window models (such as GPT-4 Turbo and long-text models like Doubao) or split the text into segments for processing. In multi-turn dialogues, if the content is substantial, you can briefly review key information in the Prompt to avoid the AI “forgetting” issue.

Multimodal: Multimodal, AI’s Sense Ability

Multimodal (multimodal) means AI can process and understand multiple types of information—such as text, images, audio, and video—at the same time, breaking the real-world limitation of single-text interaction. It deeply simulates human multi-sensory abilities of “seeing, hearing, speaking, and reading,” which is also one of the core directions of current AI technology development. For example, Baidu’s Wenxin large model 4.5Turbo as a multimodal model can already achieve mixed training for text, images, and video. Its multimodal understanding performance has improved by more than 30%.

As multimodal technology matures, AI can interact more like humans. For instance, you can send AI an image plus a text prompt—“Help me turn this landscape image into a watercolor style, and write a caption as well.” AI can understand both the image content and the text requirements, and complete an end-to-end creation with ease.

Application Layer — Turning AI into a Tool That Gets Real Work Done

With the “brain” of the basic layer and the bridge of the interaction layer, the application layer is the toolkit that deploys AI into specific scenarios and solves real problems. Its core is converting AI capabilities into products or services that can be used directly.

Agent: Intelligent Agents, AI’s Autonomous Workers

An Agent (AI intelligent agent) is an AI system with autonomous decision-making, dynamic planning, and autonomous execution capabilities. It’s like a worker who doesn’t need to be managed. You only need to give it the final objective, and it will autonomously break down the task, call tools, and solve problems without requiring step-by-step human command. In complex and uncertain application scenarios, Agents can analyze task objectives on their own and complete a positive loop of self-reflection and result feedback.

What fits user usage habits is that Agents can remember personalized preferences. For example, based on the hotels users like, the travel destinations they prefer, and the routes they want to plan, they can implement tailored information search and execution. Even more, they can learn from mistakes in the previous instruction so that future content generation and outputs are more closely aligned.

Workflow: Workflows, Standardized AI Processing Procedures

Workflow (workflow) is the process of breaking down AI tasks into step-by-step, standardized, and repeatable execution flows. It clarifies the execution order of each step, the responsible parties, and the output results. It’s like an AI assembly line that enables tasks to be executed efficiently and reliably. AI Workflows cleverly design execution steps for AI, just like LEGO instructions, allowing both users and large models to carry out tasks according to a defined SOP and improve production efficiency.

For example, in a company producing craft goods, they developed more than 120 types of standardized workflows covering the entire chain of “creative inspiration — style transfer — product editing — 3D rendering,” relying on AI drawing tools. This enables a closed-loop output from natural-language descriptions to deliverable renderings. For a single design task, the time was reduced from 5 days to 1.5 days, improving efficiency by more than 70%.

Plugin: Plugins, Efficiently Extending AI Capabilities

Plugin (plugin) is a small tool that adds specific functions to AI. It’s like giving AI plugin components to expand its capabilities. By installing plugins, you can quickly unlock new application capabilities without retraining the model. In real application scenarios, ordinary users can install plugins according to their own needs, while enterprises can develop customized plugins to fit business scenarios—greatly lowering the cost of deploying AI applications.

Specifically, AI uses Skills to think about tasks and, when needed, calls Plugin to obtain information or perform actions. Plugins follow a unified MCP protocol: plug-and-play, swappable at any time, and capable of connecting to third-party services and APIs—becoming the high-performance expansion mechanism for the whole system.

Patch Layer — An Efficient AI Error-Correction Mechanism

AI can make mistakes and can also “talk nonsense.” The core role of the patch layer is to correct AI’s errors, improving the accuracy and reliability of AI output, making AI run more reliably.

Hallucination: AI Hallucinations—Can It Really Spit Out Nonsense?

Hallucination (AI hallucinations) refers to content generated by AI that appears reasonable and fluent, but in fact is inaccurate, fabricated, or inconsistent with facts. Yet AI outputs such error information with high confidence, which is also one of the main pain points of current generative AI. This is already a relatively common shortcoming in AI-generated content: fake academic citations, invented nonexistent data, misinterpretations of facts, or fictional people or events are all frequently seen. For example, if an unoptimized LLM answers medical questions, it may provide incorrect diagnosis and treatment advice, creating serious potential risk and crisis.

Real-time tool calling and limiting output formats can effectively reduce how often AI hallucinations occur. Currently, the industry mainly solves this through methods such as RAG technology, confidence calibration, source attribution/traceability labeling, and real-time feedback and correction. Among them, RAG is the most commonly used and effective solution, reducing hallucination error rates by more than 70%.

RAG: Retrieval-Augmented Generation, the AI Knowledge-Checking Super Tool

RAG (Retrieval-Augmented Generation, retrieval-augmented generation) is the core technology for addressing AI hallucinations and knowledge lag. Simply put, it means that before AI generates content, it first checks the sources properly. It retrieves relevant, accurate information from an external knowledge base, and then combines that with its own capabilities to generate content, effectively giving AI an attached knowledge base.

In the healthcare sector, by using RAG technology to incorporate hospital medical records and medical guidelines into an external knowledge base, the accuracy of LLM-generated diagnosis and treatment suggestions increased from 65% to 92%. In the finance sector, RAG combines the latest policies and market data to generate compliant and accurate industry analysis reports, reducing error rates by 80%. Compared with traditional generative AI, a RAG-enhanced system shortens the knowledge update cycle from months to minutes, significantly reduces deployment costs, and makes generated content traceable—meeting audit requirements.

Connection Layer — Building AI Systems That Communicate Seamlessly

Different AI modules need to achieve interconnectivity through the connection layer, ensuring that data and capabilities flow smoothly. This is the key to scaling AI deployment and adoption.

MCP: Model Context Protocol, AI Standardized Interface

MCP (Model Context Protocol, model context protocol) is a standard protocol framework proposed by Anthropic and released as open source. It is designed to standardize the interaction methods between large language models and external data sources and tools. It is often referred to as the “TYPE-C interface” for AI applications—providing a standardized way to connect peripherals. MCP provides a unified interface for connecting AI models to different data sources and tools, enabling a common connection method.

The emergence of MCP breaks through the technical capability boundaries of LLMs. It allows AI applications to access local and remote resources in a relatively uniform way, enabling more efficient and flexible integration and reducing the connection cost between AI and external tools. At present, you can experience MCP capabilities at the Volcano Ark Experience Center, supporting choices of multiple models, multiple MCP servers, and tools.

API: Application Programming Interface, the AI Data Channel

API (Application Programming Interface, application programming interface) has long served as the data channel between different software and systems, helping achieve data interoperability and functional linkage with ease—without needing to develop everything from scratch. Nearly all AI deployment scenarios are inseparable from APIs. For example, enterprises integrate ChatGPT’s API into their customer service systems to quickly implement intelligent customer service; self-media platforms integrate AIGC’s API to enable batch generation of copy and images; e-commerce platforms integrate AI translation APIs to automatically translate product descriptions into multiple languages, widely covering overseas markets.

Ordinary developers can quickly build AI applications by calling publicly available APIs without needing to build underlying models. Enterprises can achieve deep integration by binding AI capabilities with their own business systems through APIs, helping automate workflows. At present, the call latency of mainstream AI APIs has already been reduced to within 100ms, and stability reaches 99.9%, meeting enterprise-level application needs.

Conclusion: Embrace the Intelligent Age and Seize the High Ground Amid AI Tech Trends

The wave of technological iteration never stops, but often only those who understand the underlying principles can better master the technology. This AI core concepts primer helps everyone deeply understand the underlying logic and key keywords of AI technology. It’s not only about keeping up with the pace of the times, but also about enabling more partners to borrow AI precisely in both work and creation—truly transforming AI tools into core productivity that improves efficiency.