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Computational Question Solver With Step-by-Step Explanations

Added Jun 2025 3 design docs

When a student asks a computational question, the answer alone rarely helps; what builds understanding is seeing the method. Calculators give results without reasoning, and generic chatbots give reasoning of uneven reliability. A focused tool that takes computational and analytical queries and returns structured, stepwise solutions fills a real gap for learners in mathematics, science, and engineering courses.

The intern builds this solver as a Streamlit application in Python, with OpenAI models providing the computational reasoning. A user types a query such as an equation to solve, a unit conversion, a statistics question, or a formula to interpret, and the application returns the answer together with a clearly formatted step-by-step derivation. The intern engineers the prompt scaffolding that pushes the model to show its work in a consistent structure, implements input parsing that distinguishes question types, and adds guardrails that flag when a result should be double-checked, an important honest-engineering habit given that language models can make arithmetic mistakes.

The project teaches structured prompt design, output formatting for readability, and the limits of LLM computation, giving the intern both a polished portfolio piece and a working understanding of when to trust, verify, or constrain model-generated calculations.

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