Mastering AI: A Guide to Industrial-Grade Large Model Systems

Discover the true power of AI large model systems and avoid common misconceptions. Learn to build industrial-grade models for a successful tech career.

If you frequently read my column, you've significantly improved your understanding of large model systems and clarified the following questions:

• What is an AI large model system?

• Why is it the new generation of application platforms?

• What is OpenAI's ultimate goal in this tech revolution?

You've also learned how to use popular open-source tools to quickly build "prototype systems."

But remember, don't get lost in the illusion of "rapid progress." Understand that open-source tools are only demo versions, not true large model systems.

Common Misconceptions (Weaknesses)

Why is this important? Because open-source tools are mainly for quick prototyping, not designed for industrial-level systems.

Let's review the most common misconceptions for beginners.

Misconception 1: Treating LangChain and AutoGPT as Real LLM Systems

LangChain, with its chain-calling method, is easy to learn but can't handle real production traffic. It's not just about the chain-calling method or Python being unsuitable for production; the gap is much larger.

Industrial applications require several systems working together—offline, near-line, and online—to ensure excellent performance and stability.

If you want to improve your career skills, don't waste time on open-source code from prototype verification projects like LangChain and AutoGPT. Instead, focus on high-quality open-source projects supported by commercial companies; this will directly benefit your career.

Tech companies invest heavily in their open-source software because it brings them real profits. For example, Google's staff are core members of Android and Kubernetes communities, allowing them to set commercial standards. Oracle benefits from MySQL, which drives sales of its paid databases.

In summary, view "open-source" objectively and choose suitable projects for learning. Use open-source as a development tool, not just a hacker's passion.

Misconception 2: Overvaluing Embedding Retrieval as Memory Enhancement

Though vector (embedding) retrieval seems novel, it's a common technique in content recommendation systems. The method of using embeddings in prompts to retrieve external memory fragments is just a variant of literal matching with clear drawbacks.

You can't find the most relevant documents because the document's semantics are fragmented from the start. Plus, open-source vector retrieval can't meet industrial performance and data volume requirements.

Even if you use vector retrieval for external memory enhancement, problems like rapid index expansion and system slowdown arise without industrial-level AI architecture support.

Misconception 3: Ignoring Quality Issues in Open-Source Models

Open-source models like ChatGML and Llama can't meet commercial needs directly. They might be barely sufficient for small automated tools, but not for commercial scenarios where customers pay.

In the commercial process, model customization is inevitable. Simple API tweaks for OpenAI's models aren't true fine-tuning; they are just shallow patches.

True domain fine-tuning requires custom models, large-scale distributed training, reinforcement learning, and MoE (Mixture of Experts) to ensure no errors and high performance in commercial systems.

Issues like ReAct loops, hallucinations, unrecognized commands, and security breaches are common. The root cause of these misconceptions is that AI large model systems are still rapidly commercializing, with core technologies shared within small circles.

Solutions (Strengths)

My column will help you fill these gaps and address the issues mentioned. As the course progresses, you'll see the complete picture of industrial-grade large models.

Theoretical Knowledge of AI Large Model Systems

To understand the strength of industrial-grade models, you need solid AI theory knowledge. Simply put, a model is a function simulating human intelligence. Training a model means solving this function's unknown variables.

Next, feature engineering helps the model better understand training data by transforming sample data into finer or higher-dimensional spaces.

Once you grasp these concepts, you'll be ready to train an industrial-grade model. I'll then teach you the common algorithms in AI's three main schools, their pros and cons, and their applications. You'll learn to integrate them for different industrial AI scenarios.

Understanding AI algorithms will ease your entry into large model knowledge. Starting with pre-trained models (PTMs), I'll explain why many large model techniques originate from PTMs and why they were first widely used in vision.

You'll also learn about the evolution of modern large language models and the historical context, including tech revolutions and the competition between Google and OpenAI.

How to Train a Large Model

With enough theoretical knowledge, we'll discuss how to build offline data engineering and model training systems for actual industrial scenarios. This will enable you to independently train models and perform online real-time updates.

From scratch, I'll teach you multi-machine, multi-card distributed training to create a true large model. You'll understand why each OpenAI training session costs millions.

Usually, you'll only need to fine-tune models. I'll share tips to speed up this process and teach you how to use RLHF (Reinforcement Learning from Human Feedback) to fine-tune models, their suitable scenarios, and their benefits.

How to Build an Industrial-Grade AI System

An industrial-grade AI large model system customizes the model for its business scenario, driven by data, rather than using a general model.

First, learn strategy modeling methods for AI systems: transforming business problems into mathematical ones, modeling them, and converting them into engineering problems. You'll learn to choose suitable algorithms for different scenarios.

In AI content recommendation services, you'll learn how to handle real-time scenarios and adjust algorithms to control online metrics, essential for AIGC (AI-generated content) systems.

To stay competitive, design system modules tailored to online services. This will make your system a strong competitor and a step towards embodied intelligence for LLM applications.

To reduce inference costs in AIGC systems, model miniaturization is essential.

For external memory issues, learn to build an industrial-grade retrieval-augmented system. This will be the main external memory of the prompt engine and a key to trustworthy AI, sourcing data from the powerful knowledge representation and retrieval capabilities of your AIRC system.

A secure risk control module is essential for industrial robustness, ensuring your commercial system runs stably under real risks.

Thought Question

What do you think are the purposes of Llama3 and Qwen's open-source initiatives?