LangChain prompt template

A LangChain prompt template is a reusable string — or array of chat roles — that inserts dynamic variables into a fixed prompt skeleton so that large language models (LLMs) receive consistent, well-structured instructions. Declared using PromptTemplate.from_template, it uses curly braces for placeholders — “You are an expert. Summarize {text} in {style}” — and checks for required keys at runtime. Templates can be chained together: a system message sets the behavior, a human message conveys user input, and an optional AI example sets the tone. They accept Jinja2 functions or filters for on-the-fly formatting, allowing locale-specific dates or token truncation. Because the prompt logic is out of code, teams can A/B test versions, save them to JSON or a CMS, and update to production without redeploying. When combined with LangChain’s LLMChain, the tooltip template feeds user data, extracts context from a vector database, and outputs a ready-to-use tooltip, reducing boilerplate code, reducing bloat, and making tooltip development a maintainable resource.