Introduction to LLMs for Developers

This course is a Chinese adaptation of the three-part Large Language Model (LLM) series co-developed by Andrew Ng and OpenAI. It covers Prompt Engineering, building systems based on the ChatGPT API, LangChain application development, and using LangChain to access private data. The course provides an in-depth yet accessible introduction to leveraging LLM capabilities to develop applications featuring summarization, inference, transformation, expansion, and chat functionalities.

Master Prompt Engineering and the LangChain framework to become an AI application developer in the era of large models.

1. Distinguish between Base LLMs and Instruction Tuned LLMs.
2. Apply four specific tactics to write clear and specific instructions (delimiters, structured output, condition checks, and few-shot prompting).
3. Implement strategies to give models "time to think" by specifying task steps and requiring independent problem-solving.
4. Implement Structured Text Processing: Summarize and extract specific information from single or multiple documents while controlling output length and focus.
5. Perform Automated Text Analysis: Categorize sentiment, identify specific emotions, and execute zero-shot topic classification using prompts.
6. Execute Multi-modal Transformations: Translate between languages, convert data formats (e.g., JSON to HTML), and programmatically correct grammar/spelling.
7. Distinguish between Base LLMs and Instruction-Tuned LLMs while understanding the impact of tokenization on model behavior.
8. Implement structured system architectures using System, User, and Assistant message roles to define model personas.
9. Apply classification and moderation techniques to evaluate user inputs for safety and routing.
10. Implement Prompt Chaining: Decompose complex tasks into manageable sub-tasks to improve reliability and reduce costs.