Key Fact Extraction Service for Reviews and Articles

Added Jun 2025 3 design docs

Useful facts arrive wrapped in prose: a product review contains the model name, the complaint, and the star-worthy feature; an article contains dates, figures, and claims. People and programs need those facts as clean fields, not paragraphs, and manually copying them out is slow and error-prone. Teaching a language model to read text and return exactly the structured data points requested is one of the most broadly applicable skills in modern software.

The intern builds an extraction service that does this as a Python Flask application. Users or client programs submit text, such as product reviews, article excerpts, or feedback entries, to an API endpoint along with the kind of extraction desired, and LangChain-structured prompts direct the language model to return defined fields, for example product attributes mentioned, sentiment, named entities, dates, and key claims, in a consistent parseable format. The intern designs the extraction schemas, implements parsing and validation that reject malformed model output rather than passing it through, and builds a simple demonstration page where pasted text is transformed into a structured record.

The project teaches the intern to treat an LLM as a data-processing component with contracts: schema-first prompting, output validation, and API design in Flask, the exact pattern behind commercial document and review intelligence products.

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