Summary
In this chapter, we dived into building complex workflows with LangChain and LangGraph, going beyond simple text generation. We introduced LangGraph as an orchestration framework designed to handle agentic workflows and also created a basic workflow with nodes and edges, and conditional edges, that allow workflow to branch based on the current state. Next, we shifted to output parsing and error handling, where we saw how to use built-in LangChain output parsers and emphasized the importance of graceful error handling.
We then looked into prompt engineering and discussed how to use zero-shot and dynamic few-shot prompting with LangChain, how to construct advanced prompts such as CoT prompting, and how to use substitution mechanisms. Finally, we discussed how to work with long and short contexts, exploring techniques for managing large contexts by splitting the input into smaller pieces and combining the outputs in a Map-Reduce fashion, and worked on an example of processing...
