Prompt Templates and Output Parsers
Explore how to design reusable prompt templates with LangChain that separate static structures from dynamic data. Understand how output parsers transform unstructured model responses into reliable Python objects, from plain text to validated schemas with Pydantic. This lesson helps you build robust chains combining templates, models, and parsers essential for scalable LLM applications.
In the previous lesson, you learned how to invoke chat models with structured message lists. But every time you wrote a prompt, you likely hardcoded the entire string, swapping out words manually for each new request. That approach breaks down fast. Change the product name, swap the user’s question, or adjust the tone, and you are rewriting strings from scratch. Prompt templates solve this by separating the static structure of a prompt from the dynamic data that fills it at runtime. Think of it like a form letter where the body stays the same but the recipient’s name and details change with each send. This lesson introduces LangChain’s template classes for building reusable prompts and then tackles the other side of the pipeline: transforming the raw text that comes back from the model into structured Python objects your application can actually use.
LangChain provides two primary template classes for this purpose. PromptTemplate uses Python-style {variable} placeholders and produces a single formatted string, which suits completion-style models. ChatPromptTemplate structures templates around message roles such as system, human, and AI, returning a list of typed message objects that chat models expect. When you call .invoke() on either template with a dictionary of variables, it returns the formatted prompt or message list, ready to pass directly to the model. This pattern is foundational because it enables you to compose templates and models into reusable pipelines called ...