| # Shipping — resume / LinkedIn copy for Project 4 |
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| > Everything below is copy-paste ready. Quantified. Honest. No fluff. |
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| --- |
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| ## One-line resume bullet (tune length to your resume format) |
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| **Preferred (single line, ~35 words):** |
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| > Built a production-grade document-extraction service (invoices, receipts, SEC 10-Ks) — FastAPI + Pydantic + GPT-5 with schema-driven structured outputs, streaming SSE + async batch API, and a fine-tuning experimentation pipeline; 0.94 F1 on receipts at $0.012/doc, benchmarked 3 model tiers, deployed to Hugging Face Spaces. |
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| **Alt (two lines, if you have room):** |
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| > Built a production-grade LLM document-extraction service handling invoices, receipts, and SEC 10-K filings. FastAPI + Pydantic v2 + OpenAI structured outputs; React + R3F frontend; Docker + GitHub Actions CI. |
| > Benchmarked gpt-5-nano / mini / full on 10 records with a self-consistent P/R/F1 harness — nano is Pareto-optimal (0.896 micro F1, $0.012/doc). 96 tests. [Live demo](https://huggingface.co/spaces/aditya0103/structured-data-extractor) · [Code](https://github.com/adityapatel007-byte/structured-data-extractor). |
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| --- |
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| ## LinkedIn post (~230 words — the diagnose loop is the hook) |
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| I just shipped a 3-week LLM project I actually want to talk about: an end-to-end |
| document extraction service (invoices, receipts, SEC 10-K filings → schema-validated |
| JSON). |
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| The interesting part isn't the "it works" — it's the measurement. |
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| Once the pipeline was live I built a small P/R/F1 evaluation harness. First run |
| on receipts: **micro F1 0.94, $0.012 per document, 6 seconds latency** on |
| gpt-5-nano. Solid baseline. |
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| Then I benchmarked gpt-5-nano vs gpt-5-mini vs gpt-5 (full) at the same |
| reasoning-effort setting. Nano won on micro F1 — the bigger tiers only lead on |
| macro F1 (rarer fields). **Pareto-optimal at $0.36 total spend.** That's a |
| number I can defend in a review meeting. |
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| 10-Ks were the hard part. First live eval: F1 0.56 — well below receipts. The |
| per-field breakdown pointed at unit-of-measure ("in millions" → absolute |
| dollars) as the biggest miss. I wrote a targeted prompt fix with worked |
| examples, re-ran, watched the numbers. |
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| Result: **revenue F1 doubled (0.25 → 0.50). total_equity went from 0.00 to 0.29. |
| Aggregate F1 flat — because filing_date regressed.** |
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| That's the story I care about. Not "the model got better." The story is: I have |
| a harness that tells me exactly which of my hypotheses paid off and which |
| didn't, so v2.3 gets chosen from data instead of instinct. |
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| **Stack**: FastAPI · Pydantic v2 · OpenAI structured outputs · React · Motion · |
| Three.js · Docker · GitHub Actions · Hugging Face Spaces. |
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| Live: [huggingface.co/spaces/aditya0103/structured-data-extractor](https://huggingface.co/spaces/aditya0103/structured-data-extractor) |
| Code: [github.com/adityapatel007-byte/structured-data-extractor](https://github.com/adityapatel007-byte/structured-data-extractor) |
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| #LLM #MLOps #Python #FastAPI #Hiring |
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| --- |
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| ## Two-paragraph project blurb (for a portfolio site / cover letter) |
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| **Structured Data Extraction Service.** A production-grade LLM pipeline that |
| turns unstructured business documents — invoices, receipts, SEC 10-K filings — |
| into schema-validated JSON with per-field confidence, cost accounting, and a |
| reproducible evaluation harness. Built on FastAPI + Pydantic v2 + OpenAI |
| structured outputs; frontend in React + Motion + React Three Fiber ("Paper & |
| Ink" aesthetic, no generic AI-SaaS look); deployed to Hugging Face Spaces via |
| a single-container Docker build with a health-check-gated nginx front end. |
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| **Why it's in my portfolio.** The eval harness is the point. On receipts, I |
| hit **micro F1 0.94 at $0.012/doc**, benchmarked three GPT-5 tiers and found |
| nano was Pareto-optimal (total spend $0.36 to definitively answer "which model |
| ships"). On 10-Ks, the first run came back at 0.56 — I diagnosed the failure |
| modes from the per-field table, made three targeted fixes, and watched some |
| of them pay off (revenue F1 doubled) and some regress (filing_date). That |
| iteration loop — hypothesis → intervention → measurement — is the shape of the |
| work I want to keep doing. 96 unit tests, GitHub Actions CI, section-based |
| chunking for long documents, and a genuinely usable UI. |
| |
| --- |
| |
| ## Suggested next social/portfolio drop (once v2.3 lands) |
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| If two-pass extract-then-verify ships: |
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| > Update on my document-extraction project — I shipped v2.3, a two-pass verifier |
| > that reads its own output and re-checks each money field against the source |
| > "in millions" header. Micro F1 went 0.56 → X.XX with zero new model calls |
| > per doc (verifier runs in the same completion). Details + code linked below. |
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| Empty for now. Fill in when v2.3 numbers exist. |
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