Generative AI Basics

In the past two or three years, AI has gone from something "occasionally heard of" to "appearing every day." It can write articles, draw illustrations, edit videos, summarize PDFs, and even create rough drafts of App interfaces based on your prompts. Many people ask: "How does AI do all this?" As someone learning Dify, you need to know even more: What are the differences between models? Why are some suitable for conversation and others for drawing? Why can agents understand what you say?

1. From "Judging the World" to "Creating the World"

In the introduction, we mentioned that in the development history of AI, core capabilities can be divided into two distinct stages: Discriminative AI and Generative AI.

Past AI focused on "Judging the World", with recognition, judgment, and classification as its core. Its main function was to "tell you what it is," essentially interpreting and defining existing information.

Today's AI has shifted to "Creating the World", upgrading its core capabilities to generation, creation, and simulation. Its core value is to "help you make something new." Text-to-image, text creation and summarization, video generation, multimodal understanding, agent planning and execution, and even future 3D scene generation—all these mainstream applications fall under the category of "Generative AI."

After learning the inherent laws of massive amounts of information, it performs "reassembly after understanding," ultimately generating "possibly reasonable" new results—just like someone who has read a hundred thousand books writing a new paragraph, or someone who has seen countless photos drawing the scene you describe, creating unprecedented content using learned rules.

---

2. Language Models: The Writing Transformer

If you only have time to understand one model, it must be the Transformer, the technology behind GPT, Claude, and Qwen. Its essence is simple: A super version of "Autocomplete."

Think of the "predictive text" feature in your phone's input method. When you type "Good", it might suggest "morning"; when you type "How", it might suggest "are". What Transformer does is logically similar at the foundational level, but its "vision" is broader and its "brain capacity" is larger.

• The Probability Solitaire Game: It doesn't actually "know" the answer; it calculates probabilities. It has read almost all text on the internet and, through training on massive data, learned the statistical laws of language arrangement. When you give it a prompt, it starts calculating frantically: "In the current context, what is the most likely next character?"
• From Predicting a Character to Generating an Article: Once it selects the next character, it adds this new character to the existing content and predicts the character after that. Over and over again, character by character, connecting words into sentences, and sentences into articles.
  • It not only remembers the beginning of this sentence but can also relate to context thousands of words ago through the "Attention Mechanism."
  • Therefore, it can generate complete chapters with logical deduction, emotional coloring, and even personal style.

---

3. Diffusion Models: Turning "Noise" into Pictures

How are images and videos generated? They rely on another type of model: the Diffusion Model. This is the core technology behind Midjourney, Stable Diffusion, and Sora.

A game of denoising from "something out of nothing": If Transformer is doing "fill-in-the-blank" questions, then the Diffusion Model is doing "sculpting." Imagine an old TV set full of static noise. What the Diffusion Model does is stare at this screen full of chaotic noise and, according to your instructions, forcefully "see" a clear picture out of it. This sounds like magic, but its principle can be divided into two processes: first learning to "destroy," then learning to "repair."

• Forward Process (Adding Noise): In the training stage, AI takes a clear photo (like a cat) and constantly sprinkles "noise" (like sand) on it until the photo completely turns into a "snowflake map" where no content can be seen. The AI remembers every change in this process.
• Reverse Process (Denoising): When generating an image, the AI gets a purely random noise map. It starts applying the "repair" ability learned before, removing noise step by step, trying to "restore" the image it thinks should exist.

Text is its "Navigator": If there is no prompt, the model might randomly restore the noise into a dog, a tree, or a car. At this time, your Prompt acts as a navigator. When you input "a cat eating pizza in space," you are actually telling the AI: "In the process of removing noise, please only keep those pixel structures that look like 'cat', 'space', and 'pizza', and throw away the rest." After dozens of rounds of "denoising-calibration," the originally meaningless noise eventually develops into a painting with amazing details.

The Essence of Video Generation: Video generation is actually a higher-dimensional diffusion model. It is not just generating a single picture, but generating a coherent sequence of 24 or more pictures (frames) at once. It not only has to deal with spatial noise (does it look like it), but also temporal noise (are the movements coherent), ensuring that the cat is eating pizza one second, and the next second the pizza gets smaller instead of suddenly turning into a hamburger. The model essentially "develops" a painting from noise in reverse.

---

4. Multimodal Models: AI Truly "Sees the World" for the First Time

If Transformer is a scholar who has "read ten thousand books," and the Diffusion Model is a painter with "a skillful pen," then the Multimodal Model is an "all-around generalist" that breaks sensory barriers. This is the logic behind GPT-4o and Gemini being able to understand your tone and the meaning behind your memes.

The "Synesthesia" Master Breaking Dimensional Walls: Before this, the AI world was fragmented: AI that processed text was "blind," and AI that processed images was "mute." They could not communicate directly. What the Multimodal Model does is equip AI with a "synesthesia" system. It no longer treats text, images, and sound as unrelated formats, but translates them all into the same "mathematical language."

A Universal "Rosetta Stone": Its core principle lies in "Alignment." In the mind of the multimodal model, it establishes a huge multi-dimensional space. Through massive training, it learns to map the text "a dog running on the grass" and "a photo of a dog on the grass" to almost the exact same location in this space. For a computer, one is text code and the other is a pixel matrix, completely unrelated. But for the multimodal model, they point to the same concept. It's like holding a "Rosetta Stone"; whether you send it a photo, an audio clip, or a line of text, it can instantly understand the same meaning represented behind it. Text becomes the annotation of the image, and the image becomes the embodiment of the text.

Evolving from "Reading Comprehension" to "Cognitive Reality": This leap in capability turns AI from simply "processing data" to "perceiving reality."

• Understanding Causality and Humor: Previously, if you sent AI a photo of a fall, it could only identify "person, ground, fall." Now if you send it to a multimodal model, it can tell you based on context: "This person probably slipped because they stepped on a banana peel on the ground. It looks a bit funny, but also dangerous."
• Cross-Sensory Interaction: You can take a photo of the inside of your fridge and send it, asking "What can I cook tonight?" It not only "sees" the ingredients (vision) but also calls upon recipe knowledge (text), finally giving you suggestions like a chef.

It is no longer limited to a single sense but constructs a complete cognition of the world by synthesizing vision, hearing, and language, just like humans.

---

5. Other Models

Currently, other models are suitable for more specialized scenarios and are less involved in Dify. But in the future, these models may also enter countless households, so a brief introduction is provided here.

1) 3D Generation: AI Directly Creates Rotatable Models

From "Paper Cutouts" to "Figurines": Traditional AI drawing (like Stable Diffusion) generates only a thin piece of paper; you can only see the front, and the back is blank. 3D generation models (like TripoSR, Luma) are like "cutting" the object out of the picture and instantly pinching out the back.

A Kind of Extreme "Spatial Imagination": When AI sees a "front view of a chair," it uses the geometric knowledge it has learned to brainstorm wildly: "Since the front looks like this, what should the back look like? How thick should the armrests be?" This is like an experienced sculptor who can build the full picture of an object in their mind from just one photo, and then mold it out using virtual "digital clay" (mesh or point cloud). Although it hasn't truly seen the back of this chair, based on its experience of seeing countless images, it guesses the most reasonable shape by calculating light, shadow, and structure. From "drawing a picture" to "creating an object," AI begins to have a sense of volume.

2) World Models: AI Starts to "Brainstorm Environments"

Not just drawing, but understanding "Physical Laws": 3D generation creates objects, while World Models create "universes." This is currently the most cutting-edge concept in the AI field. Previous video generation might just connect frames to make them move, but AI might not understand "why it moves like this." World Models attempt to build a "physics engine" similar to the real world in their brains through learning.

Rehearsing the Future in this "Brain Simulator": Imagine playing "Need for Speed" or "GTA"; the game engine knows a car stops when it hits a wall, a cup breaks when it falls, and water flows downwards. A World Model installs such an engine in the AI's mind.

• Understanding Causality: When it generates a video, it's not blindly guessing pixels, but deducing: "If this car turns left, how should the scene change? If a glass falls on the floor, should it bounce or shatter?"
• Predicting the Future: It can even predict the feedback the environment will give before you make a move, just like human intuition.

World Models make AI no longer just a "repeater" that mimics appearances; it starts to learn basic physical common sense like gravity, inertia, and collision by observing the world like a newborn baby, thereby deducing a logical virtual world in its digital brain.

3) AI4S (AI for Science): AI Becomes a Scientific Assistant

If the previous models are learning human language and art, then AI4S is letting AI learn "nature's language." It is no longer writing poetry or painting, but putting on a white coat and walking into the laboratory to help scientists solve the most hardcore physics, chemistry, and biology problems.

Pressing the "Fast Forward" Button for Scientific Exploration: Traditional scientific research is often a long process of "trial and error." Edison tried 1,600 materials to find a filament, and new drug development often takes 10 years and billions of dollars. The emergence of AI4S is like giving scientists a "treasure map." By analyzing massive amounts of historically accumulated experimental data, it can predict which material is most likely to succeed and which drug molecule is most effective before doing any experiments. It compresses blind exploration that would originally take years into precise calculations of a few days.

Replacing "Brute Force Calculation" with "Intuition": Before AI4S, scientists predicted weather or simulated fluids relying on extremely complex mathematical formulas (like solving partial differential equations). This is not only difficult but also extremely consumes supercomputer power, calculating slowly and easily exhausting computers. AI's approach is completely different. It doesn't hard-solve formulas but relies on "experience." This is like a seasoned basketball player shooting a basket. He doesn't need to calculate the parabolic formula, air resistance, and gravitational acceleration in his head; he relies on "muscle memory" and "intuition" after millions of practices. AI4S works on this level: by learning from the changing laws of billions of data points, it skips tedious formula derivations and directly gives predictions extremely close to real results.

AlphaFold's Folding Magic: The most famous example is DeepMind's AlphaFold. Protein is the cornerstone of life, and its function depends on its complex 3D structure (like an extremely complex ball of tangled yarn). In the past, human scientists spent decades resolving a small fraction of protein structures with great effort. AlphaFold, simply by learning from known data, predicted the structures of almost all known proteins on Earth in a short time, just like playing a speed puzzle. It wasn't doing experiments; it directly "saw through" the folding laws of biological molecules.

---

6. The New Paradigm of Human-Computer Interaction: Prompt Engineering

In the AI era, the programming language is no longer complex Python or C++, but your mother tongue. Prompt Engineering might sound fancy, but essentially it is "how to learn to speak properly to machines."

It's like you hired a knowledgeable but somewhat rigid intern. If you just vaguely command "go write a plan," he will likely give you a pile of nonsense; but if you tell him "As a senior product manager, please write a promotion plan of no less than 500 words for a young user demographic, with a lively tone," he can hand in a perfect paper.

To master this super brain, you need to master two core mental methods:

1. Persona: Give it a "Badge" First

This is the simplest and most effective trick. Before asking, tell the AI "who you are." AI is like an actor with countless masks. If you don't specify a role, it's a mediocre passerby; specify a role, and it immediately switches its original knowledge base and tone.

• Ordinary Ask: "How to write a diet recipe?" (Answer might be official and boring)
• Pro Ask: "You are a professional fitness coach and nutritionist with 20 years of experience, please help me..." (Answer becomes professional, encouraging, and focuses on scientific matching)

2. Context: Don't Let It Play "Guessing Games"

Many people think AI is stupid because they didn't explain the background (Context) clearly. Don't just give a verb; complete the "who is it for, what is the background, what format."

• Vague Instruction: "Help me write a leave application."
• Clear Instruction: "I need to ask for two days of sick leave (duration) from my boss (target) because I have a cold and fever (reason). Please help me write a leave application. The tone should be sincere but professional (style), and keep my mobile number as an emergency contact."

3. Few-Shot Prompting: Give It a Chance to "Copy Homework"

When you exhaust yourself describing rules and it still doesn't understand, it's better to directly show it two examples. Large models are essentially powerful mimics. When you only give instructions (this is called Zero-shot), it is guessing your standards; but when you give a few examples (Few-shot), it quickly analyses the patterns in the examples and replicates them perfectly.

• Human Way: "Help me translate this word into English, make it a bit poetic."
• Prompt Way: "Please translate following the style below:
  • Example 1: '花落知多少 (How many flowers fulfill)' -> 'How many flowers have fallen.'
  • Example 2: '举头望明月 (Raise head see bright moon)' -> 'I raise my head to view the bright moon.'
  • Please translate: '大漠孤烟直 (Desert lonely smoke straight)'"

---

7. The Model's "Dashboard": Key Parameters Explained

When you open Dify or other large model platforms, parameters like Temperature and Token are essentially the dashboard controlling this AI "internal combustion engine."

1. Token: AI's "Billing Unit"

In AI's eyes, text is not counted by "characters," but by Tokens. A Token is the smallest unit after text is segmented.

• Conversion Relationship: In English, 1 word $\approx$ 0.75 Tokens; in Chinese, 1 character usually corresponds to 1 to 2 Tokens (depending on the specific model's tokenization).
• Why It Matters: Almost all commercial models (like GPT-4) charge by Token count. Whether you are asking (Input) or it is answering (Output), the meter is running.

2. Context Window: AI's "Short-Term Memory"

This is the upper limit of information AI can process at one time.

• Goldfish Memory: Early model windows were small (e.g., 4k Tokens); after a dozen sentences, it forgot what your name was at the beginning.
• Elephant Memory: Current models (like Claude 3 or GPT-4-Turbo) have windows of 128k or even longer, meaning you can throw the entire "Dream of the Red Chamber" in and let it analyze Lin Daiyu's personality.
• Note: Although the window is large, the more you stuff in, the accuracy of finding information might drop (this is the "needle in a haystack" effect), and costs will skyrocket.

3. Temperature: The Valve Between Rationality and Sensibility

This parameter controls the randomness of AI output, typically ranging from 0 to 1 (some models go higher). Usually, model vendors provide a recommended temperature, which is fine for most cases, but you can also adjust it yourself.

• Rigorous Mode (0 - 0.3): Suitable for coding, math problems, data extraction. AI becomes like a rigorous accountant, giving almost identical answers every time, daring not to cross the line.
• Creative Mode (0.7 - 1.0): Suitable for writing novels, brainstorming, chatting. AI becomes a romantic poet; even for the same question, it can come up with something new every time, but it is also more prone to "talking nonsense."

---

8. Defects That Cannot Be Ignored: Hallucinations and Limitations

Before putting large models on a pedestal, we must clearly recognize their Achilles' heel: Hallucination.

4. Why Does It "Talk Nonsense Seriously"?

Remember what we said in Chapter 1? The essence of Transformer is predicting the next character. It doesn't actually "understand" facts; it just remembers "these words look smooth together." When you ask it "How is the plot of Lin Daiyu pulling up a weeping willow?", it might associate "pulling up a weeping willow" with Lu Zhishen based on pattern matching, and "Lin Daiyu" with "Dream of the Red Chamber," and then confidently fabricate a story about Lin Daiyu pumping iron in the gym. It is not lying; it is just "dreaming."

5. "Compression" and "Obsolescence" of Knowledge

• Lossy Compression: The process of large model training is actually "compressing" the knowledge of the entire internet into parameters. It's like compressing a HD movie into a blurry GIF; details (specific phone numbers, birthdays of non-famous people) are easily lost or confused.
• Time Capsule: The model's knowledge has an expiration date. Without internet search, GPT-4 might still not know who the 2024 Olympic champion is because its memory stops on the day training ended.

---

9. How to Solve It? — "Knowledge Base" and RAG: Giving AI a "Reference Book"

Since the model can't remember details (like yesterday's meeting minutes) and loves to make things up (hallucination), what can be done? The answer is: Change from "Closed-Book Exam" to "Open-Book Exam."

1. Closed-Book vs. Open-Book: The Core Logic of RAG

• Before (Pure Large Model): Like letting a student into the exam room without books, answering questions purely from memory. If asked "Who is Li Bai," he can recite it; but if asked "What is Article 3 of our company's new attendance policy issued last week," he not only can't recite it but might invent one to save face.
• Now (RAG Technology): We allow this student to bring a thick "Reference Book" (this is your Knowledge Base). When encountering a question he doesn't know, he looks it up in the book first, finds the corresponding paragraph, and reads it out or summarizes it for you.

This is RAG (Retrieval-Augmented Generation). It doesn't force AI to "memorize" all knowledge but teaches AI "how to look up information."

2. How Does It Work? (Three Steps)

RAG divides the AI's answering process into three steps, completely curing its "talking nonsense disease":

• Step 1: Retrieval — "Finding the Cheat Sheet": When you ask: "What is our company's reimbursement limit?" The system won't throw the question directly to AI but will first quickly search through your "Enterprise Document Library." It finds that page 5 of the "2024 Financial Reimbursement Manual.pdf" mentions "limit," so it "extracts" this paragraph.
• Step 2: Augmented — "Passing the Cheat Sheet": The system packages the user's question and the "standard answer fragment" just extracted and quietly slips it to the AI. At this time, the instruction given to AI actually becomes: "The user asks what the reimbursement limit is. Please answer the user's question based on the content of this financial manual (reference material). Do not make things up yourself."
• Step 3: Generation — "Writing the Answer": The AI reads the reference material and answers with confidence: "According to the financial manual regulations, the employee single reimbursement limit is..."

3. The "Black Tech" Here: Vector Database

You might ask: "How does the computer know which paragraph is the 'reference material' I'm looking for? Do I rely on keyword search?" Not just keywords. Behind RAG is a smarter librarian called a "Vector Database." It doesn't look at the literal words but the "meaning."

• Traditional Search: You search for "apple," and it can only find articles with the characters "apple."
• Vector Search: You search for "delicious red-skinned fruit," and it can help you find "apple."

In the knowledge base, AI translates all documents into strings of numbers (vectors). When you ask a question, it calculates the "similarity" between your question and the document content. So, even if your question words are completely different from the words in the document, as long as the meaning is close, it can accurately find that paragraph for AI reference.

Currently: Technologies like RAG and enterprise knowledge retrieval are changing with each passing day. Only some simple concepts are introduced here, and more detailed chapters will discuss them later.

4. Why Is This Important to You?

This is why we need platforms like Dify.

• Data Privacy: You don't need to take the company's confidential data to "train" the model (that is extremely expensive and unsafe); you just need to put the documents into Dify's knowledge base, and AI can "read" and understand them, and the data never leaves the house.
• Instant Update: Company policy changed? Just replace the document, and AI can answer the latest content in the next second without retraining.
• Eliminate Hallucinations: By limiting AI to "answer only based on the knowledge base," you can reduce its nonsense rate to a minimum, turning it from a "joke teller" into a reliable "customer service expert."