Customizing Chain Logic

In previous lessons, you learned how to create sequential chains to perform multi-step operations. These chains are powerful for connecting standard LangChain components like prompts and LLMs.

But what if you need to do something unique? What if you need to process data in a specific way before it reaches your LLM, or format its output afterward?

Standard chains are great, but they can't handle every scenario. Custom logic allows you to:

• Pre-process inputs: Clean, validate, or transform user input before sending it to a prompt or LLM.
• Post-process outputs: Parse, filter, or reformat LLM responses for display or further use.
• Integrate external logic: Call your own Python functions, external APIs, or apply conditional routing.
• Handle complex data transformations: Convert data types, merge information, or apply business rules.