What & Why LLM Application Development ​

Currently, the hottest technology in the tech world is AI! With the release of GPT-4 today and Gemma tomorrow, these large models are increasingly impressive, each claiming to potentially replace human jobs. Industry leaders like Li Yanhong and Jensen Huang have commented on the notion that "programmers will disappear." While that sounds exaggerated, the obsolescence of junior programmers seems inevitable. Recently, Alibaba Cloud even assigned an official employee number—AI001—to its AI Tongyi Qianwen, declaring that 20% of the company’s code will be written by AI in the future.

These Large Language Models (LLMs) are akin to brains, with companies like OpenAI and Google making them "smarter." However, applying this intelligence in real-world business contexts is where LLM application development comes in, which is the primary focus of our course.

Currently, there are two popular directions in LLM application development: RAG application development and AI Agent development.

RAG applications combine LLMs with external knowledge retrieval in a generative AI design pattern. By retrieving relevant knowledge and integrating it into prompts, these models can reference information beyond their training sets while effectively reducing issues like "hallucinations."

AI Agents leverage LLMs' reasoning and decision-making capabilities, enhancing them with planning, memory, and tool invocation to create agents that can think independently and progressively achieve set goals.

What & Why LangChain? ​

Alongside the emergence of various LLMs, the LangChain framework was born. First open-sourced at the end of 2022, it quickly became one of the hottest open-source projects, maintaining its popularity since then.

As of now, it has surpassed 80k stars on GitHub. LangChain is a development framework built around LLMs that connects various models, allowing for quick model switching, while also providing tools, components, and interfaces for seamless integration with external systems, greatly simplifying the LLM application development process.

For learning LLM application development, LangChain is a great choice. The framework divides LLM development into distinct modules, enabling us to master each aspect effectively.

However, this course doesn't focus on which component or interface of LangChain to use or how to pass parameters, as that’s well-explained on the official website. After exploring the site, users may find themselves confused about which components to choose for different scenarios, let alone customizing their components based on specific business needs.

Many components in LangChain are supported by corresponding theoretical foundations. Therefore, the course aims to delve into the source code based on the LangChain framework, learning how and why different components are implemented. This way, we can develop LLM applications more adeptly according to various business scenarios.

The course is divided into three parts: an introductory section, a foundational section, and a practical section.

In the introductory part, we will present the development history and basic principles of large models in layman's terms, demystifying AI. We'll also introduce the LangChain framework, covering its installation process and providing a hands-on case study with a local knowledge base Q&A bot to help you quickly get started with LLM development and appreciate the charm of LangChain.

The foundational part will explore each aspect of LLMs, studying the implementation and usage of the six core modules in the LangChain framework: model IO, data retrieval, memory, chains, agents, and callbacks, equipping you with the skills to construct a functional LLM application.

Closing Remarks ​

Finally, I want to share a quote from a prominent internet figure during a recent AI public speech to inspire everyone:

"I firmly believe that large models are the best productivity tools ever invented by humanity, enhancing our personal capabilities. Therefore, generally speaking, having large models does not mean you will be unemployed, as they improve work efficiency and even unlock capabilities that were previously unavailable."

AI's intelligence brings us pressure but also unprecedented opportunities. As long as we believe in AI, embrace it, and learn from it, we can ride this new wave and benefit from the dividends of this new era.