Free LangChain for LLM Application Development Course by Ernest Ng

What is LangChain for LLM application development?

LangChain for LLM application development isDeepLearning.AILaunched an online course featuring LangChain founders Harrison Chase and Andrew Ng. The course teaches how to use the LangChain framework to extend the application scenarios and functionality of language models (LLMs). It covers model invocation, prompt and response parsing, LLM memory management, operation chain creation, document Q&A and LLM agent development. With 8 video lessons and 6 code samples, beginners can master how to apply LLM to proprietary data, build personalized assistants and professional chatbots, and quickly develop powerful language model applications in 1 hour and 38 minutes.

LangChain for LLM Application Development Course Objectives

• Framework Founder Teaches Learning LangChain: Learn directly from LangChain's creator, Harrison Chase, for an in-depth look at the framework's design philosophy and best practices.
• Applying LLM to proprietary data: Learn how to apply large language models to your own data to build personalized assistants and professional chatbots that meet specific business needs.
• Extending the use of LLM with advanced features: Extend the capabilities of LLM for more complex interactions and applications by learning how to use Agents, Chained Calls, and Memories.
• Rapid development of powerful applications: With the knowledge and practice provided in the course, learners will be able to develop powerful LLM applications in a short period of time and improve development efficiency.

LangChain for LLM Application Development Syllabus

• introduction: Introduce the course structure and objectives to help learners understand the LangChain framework and its role in LLM application development.
• Models, Hints, and Parsers: Learn how to call LLM, provide effective prompts and parse the responses, and optimize the prompts for better LLM output.
• memorization: Learn how to use the memory function to store conversation history, manage limited context space, and improve LLM performance in multi-round conversations.
• cable length (= 1: Create sequences (chains) of operations that combine multiple LLM calls to achieve more complex tasks and optimize the LLM workflow.
• Indexing and retrieval: Applying LLM to proprietary data using LangChain's indexing and retrieval capabilities for document Q&A functionality.
• valuation: Learn how to evaluate the performance of LLM applications, including accuracy and efficiency, and optimize applications through testing and feedback.
• act on behalf of sb. in a responsible position: Understand the power of LLMs as reasoning agents and explore how to make LLMs reason and make decisions in complex tasks.
• concluding remarks: Summarize the key points of the course, provide guidance for subsequent learning, and emphasize the importance of continued exploration and practice.
• test: Test your understanding of course content with quizzes that reinforce key concepts.

LangChain for LLM application development course address

• Course Address::DeepLearning.AI

Who LangChain for LLM Application Development is for

• beginning student: Suitable for zero-based learners to quickly get started with LangChain and LLM application development.
• Developers with some basic Python knowledge: Suitable for learners who have mastered Python programming, helping users to quickly get started with LLM application development.
• Data Scientist and Machine Learning Engineer: For professionals with a background in data science or machine learning, applying LLM techniques to real-world projects.
• Product Manager and Business Analyst: Ideal for learners focused on business applications, helping users understand LLM technology and drive business innovation.
• AI and Natural Language Servicesscience enthusiast: Suitable for learners interested in AI and natural language processing, stimulating users' interest and exploration of LLM technologies.