LangChain for LLM Application Development - Syllabus

• Introduction to LangChain for LLM Applications
  • Overview of the LangChain framework for LLM application development
  • Course goals and the possibilities of using LangChain to enhance LLM capabilities
• Models, Prompts, and Parsers
  • Calling large language models and providing structured prompts
  • Parsing responses to extract relevant data and achieve targeted outputs
  • Hands-on code examples for understanding prompt-response workflows
• Memory for LLMs
  • Implementing memory to store conversation history and maintain context
  • Optimizing limited context space using memory
  • Practical examples of using memory to enhance conversational applications
• Chains: Creating Sequences of Operations
  • Building chains for multi-step operations and workflows
  • Configuring sequences of prompts and LLM interactions
  • Code examples for constructing and implementing chains
• Question Answering over Documents
  • Applying LLMs to proprietary data for Q&A tasks
  • Customizing responses to meet specific use case requirements
  • Code examples for document-based question answering
• Evaluation Techniques
  • Evaluating LLM performance on various tasks
  • Metrics and methods for assessing accuracy and relevance
  • Code examples for implementing evaluation techniques
• Agents: Building LLM Reasoning Agents
  • Exploring LLMs as autonomous reasoning agents
  • Configuring agents to carry out complex tasks independently
  • Hands-on code examples to develop and test agent capabilities
• Course Conclusion
  • Summary of key LangChain functionalities for LLM applications
  • Final insights on using LangChain to maximize LLM effectiveness