LangChain prompt template

PG() fotor bg remover fotor bg remover
Bartosz Roguski
Machine Learning Engineer
Published: July 1, 2025
Glossary Category

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.

Want to learn how these AI concepts work in practice?

Understanding AI is one thing. Explore how we apply these AI principles to build scalable, agentic workflows that deliver real ROI and value for organizations.

Last updated: July 28, 2025