LangChain PromptTemplate Builder

A PromptTemplate is LangChain's way of creating reusable prompts with dynamic variables. You define a template string with curly-brace placeholders, then fill them in at runtime. This enables prompt reuse, testing, and chaining prompts together in pipelines (LCEL — LangChain Expression Language).

PromptTemplate generates a single string — suitable for completion models (text-davinci, older GPT). ChatPromptTemplate generates a list of messages (system, human, AI) — required for chat models like GPT-4o and Claude. ChatPromptTemplate is the standard for modern LLM applications using the messages API.

Variables are defined with curly brace syntax. LangChain will raise an error if required variables are missing at invocation time. You can also use partial_variables to pre-fill some variables while leaving others dynamic — useful for injecting API keys, dates, or system context.

LangChain Expression Language (LCEL) is a declarative way to compose chains: prompt | model | output_parser. PromptTemplate transforms your input dict into a formatted prompt string or messages, which is then passed to the model. LCEL chains are lazy — they only execute when you call .invoke(), .stream(), or .batch().

Yes. LangChain prompts are provider-agnostic. The same ChatPromptTemplate works with OpenAI, Anthropic Claude, Google Gemini, Cohere, Ollama (local), and any other provider that implements the LangChain LLM or ChatModel interface. This makes it easy to swap models without rewriting your prompts.