Prompt Template Design and Testing Workbench for Educators

Added Jun 2025 3 design docs

Teachers and instructional designers increasingly build AI-driven learning modules, such as quiz generators, explanation assistants, and feedback tools, and every one of them lives or dies by its prompts. Yet educators typically iterate on prompts by trial and error in a chat window, with no way to save what worked, compare variations, or enforce consistent output formats. Prompt engineering needs a workbench, the way code has an IDE. The intern builds that workbench as a Streamlit application in Python, using LangChain as the engine. The tool lets users create parameterized prompt templates with variables such as subject, difficulty, and student level; run them against OpenAI models; and compare outputs across template variations side by side. Output parsers enforce structured results, so a quiz template reliably yields well-formed questions and answers rather than loose prose, and chain sequencing lets users compose multi-step flows in which one prompt's output feeds the next, mirroring how real learning modules are assembled. The intern implements template storage, the comparison runner, and the parser configuration interface. The project demonstrates systematic prompt engineering as a discipline: template design, structured output enforcement, chain composition, and building a tool that turns an ad hoc craft into a repeatable process for a non-technical audience.

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