How can I customize the LangChain prompt generation logic?

To customize LangChain’s prompt generation, you can modify prompt templates, adjust input handling, or extend core classes. LangChain provides built-in tools like PromptTemplate and Chain classes that let you define the structure and logic of prompts. Customization typically involves creating templates with specific placeholders, injecting dynamic data, or overriding methods to control how prompts are assembled. For example, you might adjust a template’s wording, add context-specific instructions, or integrate external data sources into the prompt logic.

A practical starting point is using PromptTemplate to define reusable templates. Suppose you want a chatbot to answer questions about a documentation library. You could create a template like:

from langchain.prompts import PromptTemplate
template = """
Answer the user's question using ONLY the provided context:
Context: {context}
Question: {question}
Answer in 3 sentences or fewer.
"""
prompt = PromptTemplate(input_variables=["context", "question"], template=template)

This template enforces a specific response format and limits answer length. You can adjust the instructions, input variables, or formatting (like markdown) based on your use case. For dynamic scenarios, use FewShotPromptTemplate to include examples that adapt based on user input, or use partial to pre-fill variables like system roles.

For advanced control, subclass BasePromptTemplate to create custom prompt logic. For instance, you might build a template that selects different instruction sets based on the user’s query type. Override the format method to add conditional logic:

class CustomPrompt(BasePromptTemplate):
 def format(self, **kwargs) -> str:
 if kwargs.get("tone") == "formal":
 base = "Respond formally to: {query}"
 else:
 base = "Respond casually to: {query}"
 return base.format(**kwargs)

To integrate this with LangChain workflows, pass your custom prompt to chains like LLMChain or modify existing chains like RetrievalQA by overriding their from_chain_type method to inject your template. This approach maintains compatibility with LangChain’s ecosystem while letting you enforce domain-specific rules or workflows.