Interactive Prompt Engineering Workbench for LLM Experimentation

Added Jun 2025 3 design docs

Small changes in prompt wording can produce dramatically different results from a large language model, yet most students and early researchers experiment through ad-hoc trial and error in a chat window, losing track of what they tried and why one phrasing outperformed another. Educators teaching generative AI face the same gap: there is no simple environment where learners can change one variable at a time and observe its effect on model behavior. This project delivers a Streamlit workbench where users author reusable prompt templates with placeholder variables, fill them with test inputs, and run them against a language model through LangChain's PromptTemplate and chain abstractions. The intern builds side-by-side output comparison so two prompt variants can be evaluated on the same input, adds controls for generation parameters such as temperature and maximum tokens, and implements a session history panel that records every prompt, configuration, and response so experiments become reproducible. Python powers the orchestration layer, keeping the codebase approachable for beginners. By completing the build, the intern gains practical fluency in prompt design patterns such as few-shot examples and role instructions, learns how LangChain structures calls to generative AI models, and demonstrates the ability to turn an exploratory workflow into a polished, shareable web tool.

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