Introduction to How AI Large Models Work: A Must-Read Guide for Test Engineers

Understanding AI Large Models from Scratch: What Are They Really?

When we casually throw around the term "AI large model," many people don't actually know its precise meaning. Strictly speaking, "AI large model" is a rather imprecise term. To understand it, we first need to clarify the hierarchical relationships between several fundamental concepts.

The history of AI (Artificial Intelligence) can be traced back to the 1970s. Early AI was primarily based on statistics and strategy rules. In fact, AI during this period was mainly built on Symbolism and rule-based systems. The "Expert Systems" that flourished in the 1970s and 1980s are typical examples—they simulated expert decision-making by manually writing large numbers of "If-Then" rules, such as the medical diagnostic system MYCIN. The biggest limitation of such systems was that knowledge had to be manually entered; they could not autonomously learn from data, and would fail whenever they encountered situations not covered by the rules. This is precisely why modern AI later shifted toward the machine learning paradigm centered on being data-driven and self-learning.

What we refer to today as DeepSeek, Gemini, ChatGPT, and so on, all belong to modern artificial intelligence. The core technical direction of modern AI is machine learning, which is further divided into reinforcement learning and deep learning. The large models that are so popular today belong to the branch of deep learning.

It should be noted here that machine learning and deep learning have a containment relationship. Machine learning is the general term for methods that let computers automatically discover patterns from data. Traditional machine learning (such as decision trees, support vector machines, and random forests) often requires manually designed features. Deep learning is a subset of machine learning, with its core being multi-layer neural networks (Deep Neural Networks)—the more layers, the "deeper" the network, and the stronger the model's ability to express complex patterns. The biggest breakthrough of deep learning is that it can automatically learn features from raw data, eliminating the tedious process of manual feature engineering. This is the key reason it has comprehensively surpassed traditional methods in fields such as image, speech, and text.

In other words, large models are not the entirety of artificial intelligence—they are just one specific branch on the great tree of AI. Understanding this hierarchical relationship is the foundation for our subsequent discussion of large model applications and testing.

The Three Mature Application Areas of Deep Learning

In recent years, deep learning has achieved a series of breakthroughs, mainly concentrated in three mature application areas:

• Computer Vision (OpenCV): The understanding and processing of images. For example, features like automatic face-slimming, skin-smoothing, eye-enlarging, and beautification during short-video selfies.
• Speech Recognition: WeChat voice-to-text, smart speakers (Xiao Ai Tongxue, Tmall Genie), and even voice-controlled lights that cost only a few dollars—all reflect the widespread adoption of speech recognition technology.
• Natural Language Processing (NLP): The understanding of text content, semantics, and meaning.

What we now call AI large models, in the narrow sense, mainly refers to Large Language Models (LLMs) based on natural language processing. Of course, in a broader sense, images, speech, and video can also be "scaled up" using the Transformer architecture, forming large image models, large speech models, and so on.

Why AI Large Models Exploded in Popularity: The Lowering of Barriers

Many people think AI suddenly became popular, but that's not the case. There has been a great deal of technical accumulation behind it—it's just that past AI focused more on professional fields. Only people within those fields could apply it, while ordinary outsiders couldn't use it.

Computer vision technology is actually very mature, and its image recognition capabilities are extremely powerful. But it has a natural limitation: it can identify the content of an image, but it's difficult to directly convey the results to the user—ultimately, communication still has to happen through text. The same is true for speech recognition, where communication barriers between different languages limited its adoption.

It wasn't until large language models matured that the situation fundamentally changed. We can tell the AI our needs in text, and the AI can also feed back the results to us in text. This leads to a colloquial yet profound conclusion:

As long as you can chat via text, you can use AI.

It is precisely the maturity of natural language processing that lowered the barrier to using AI to an extremely low level—anyone who can type and chat can use large models. This also explains why large models could rapidly reach the general public.

How Transformers Work: The Essence of Large Models Is "Probability"

To truly make good use of large models, you also need to understand their basic working principles. The core here is the Transformer (many dictionaries translate it as "变形金刚" (Transformers, the robots), but that's actually inaccurate—it's essentially a "converter").

The Transformer was proposed by Google in the 2017 paper Attention Is All You Need. Its revolutionary nature lies in the introduction of the "Self-Attention" mechanism. Before this, processing sequence data mainly relied on RNNs and LSTMs, which had to process words sequentially, making them difficult to parallelize and prone to forgetting long-distance information. The self-attention mechanism allows the model, when processing a certain word, to simultaneously "attend to" all other words in the sentence and compute the association weights between them, thereby better understanding contextual semantics. This parallelizable structure greatly improved training efficiency, making it possible to train ultra-large-scale models on massive amounts of data, ultimately giving rise to a series of large language models such as GPT and BERT.

A Machine Translation Example

When the Transformer was first proposed by Google, it was used for machine translation. Let's take the translation of "I love you" into Chinese as an example, to simplify our understanding of the entire process:

1. Encoding: First, the input English words are encoded into groups of vectors. At this point, they are no longer letters but numerical vectors.
2. Vector Space Computation: These vectors enter a vector space, where the model searches for semantically similar vectors. For example, after "I love you" goes in, it might find multiple candidates such as "我爱你," "我喜欢你," "我稀罕你," and so on.

The "vectors" mentioned here are backed by "Word Embedding" technology. It maps each word to a coordinate point in a high-dimensional space (usually hundreds to thousands of dimensions), where semantically similar words are also close together in that space. A classic example is: "King" - "Man" + "Woman" ≈ "Queen," which shows that vectors encode not only word meanings but also the semantic relationships between words. It is precisely because of word embeddings that computers can perform mathematical operations on "meaning"—this is the foundation for large models to understand and generate language.

3. Decoding and Probability Selection: The model doesn't directly map to an answer, but rather derives it word by word. It first computes the first word "我" (I), then derives the next word based on "我"—finding that "爱" (love) has the highest probability and "喜" (like) has a lower probability, so it chooses "爱"; then it derives "你" (you). Finally, it outputs "我爱你" (I love you).

Core Conclusion: The Essence Is Probability Estimation

This process reveals the most fundamental characteristic of large models: their computation is probabilistic.

This means that if you ask it to translate the same sentence 100 times, the results may all be different; if you repeatedly ask the same question, each answer will also vary. Once you understand this, you've grasped the soul of large models—they mainly "guess" results based on probability, rather than through precise computation or authoritative citation.

Strengths and Weaknesses of AI Large Models: A Complete Analysis of Applicable Scenarios

Since the essence of large models is probability, we can deduce the boundaries of their applicability.

What Large Models Are Good At

• Literary Creation: Scenarios that require randomness, variation, and creativity. It's like chatting with a person—if the other party always replies with the same fixed few words, you'll quickly lose interest; whereas probabilistic, varied expression actually makes the conversation more engaging.
• Divergent Thinking: When you need bizarre, unexpected content, the probabilistic nature becomes an advantage.

What Large Models Are Not Good At

• Precise Computation: Give it a set of complex equations, and it may not compute faster or more accurately than you would with a calculator. This is also why early users often used complex calculations to test the intelligence of ChatGPT and DeepSeek.
• Authoritative and Accurate Answers: In fields like law, finance, and medicine where "no mistakes are allowed," large models should not answer in an overly confident tone. During training, they are usually guided to admit "I don't know" and advise users to consult actual doctors, lawyers, and other professionals. This also brings up a widely discussed problem with large models—"hallucination," where the model generates content that appears reasonable and is stated with certainty but is actually incorrect or fabricated. Hallucination is precisely a byproduct of the probabilistic generation mechanism: the model pursues the "most probable next word" rather than the "factually correct next word." Therefore, extreme caution must be exercised in scenarios with very high factual requirements.

It should be added that large models are iterating rapidly. Taking the evolution from GPT-4 and GPT-4o to GPT-4.5 as an example, precise computation capabilities have made significant progress. So the "weaknesses" mentioned here are more of a relative comparison and don't mean they are completely unusable in computational scenarios.

Looking at Large Models from a Test Engineer's Perspective

For test engineers, this analysis of strengths/weaknesses can directly guide practice.

Testing Phases Suitable for AI

• Evaluating Requirements and Designing Test Cases: Testing work itself requires considering all sorts of bizarre and unexpected edge cases. Here, AI's probabilistic nature and divergent capabilities are precisely an advantage.

Testing Phases Not Suitable for AI

• Debugging Errors: When you have AI locate and debug bugs, it easily gets stuck on the wrong track and can't escape from a dead end. This is its current obvious limitation.

Two Approaches to Testing Large Models

When we want to test an AI large model product, there are two mainstream methods:

1. Using AI to Test AI: Low cost, fast speed, suitable for large-scale automated evaluation.
2. Manual Testing: The most expensive and slowest, but human evaluation of large models is one of the gold standards, especially for content involving humanities and subjective judgment, where the results of manual evaluation are the most accurate.

Conclusion

If you really can't remember all these concepts, just remember one word—probability. Once you understand the essence that large models "mainly guess results based on probability," you'll naturally develop an intuition for which scenarios are suitable for them and which scenarios require human backup.

For traditional test engineers, the AI wave is not a threat but an opportunity. Secondary development around large models (such as AI IDEs, AI customer service, AI English speaking courses, etc.) is giving rise to a large number of new applications and jobs. Mastering the principles and testing methods of large models is precisely our starting point for "getting a head start on the future."

Key Takeaways