# Application Messages

Short, role-specific notes for applications/DMs. Replace `[link]` with your repo
or 60-second demo. Keep them under ~90 words.

## AI Product Intern

Hi [Name] — I'm applying for the AI Product Intern role. I recently built
FinanceFlow AI, a source-backed document assistant where every answer carries
citations and the system abstains when evidence is missing. I also wrote a
golden-dataset evaluation that measures citation coverage and unsupported-answer
rate — treating a correct refusal as a success. It's the kind of trustworthy-AI
product thinking I'd want to bring to your team. Demo + code: [link].

## Product Operations Intern

Hi [Name] — applying for the Product Operations Intern role. I built an
end-to-end document-Q&A workflow (upload → retrieve → answer → cite → evaluate)
that replaces manual PDF lookups, with logging and a measurable evaluation
harness runnable from the UI and CLI and gated in CI. I'm comfortable turning a
messy process into something measurable and reliable — happy to walk through it.
Demo + code: [link].

## AI Workflow Intern

Hi [Name] — applying for the AI Workflow Intern role. I built FinanceFlow AI
around the core document-AI loop: ingest → chunk → retrieve → answer → cite →
evaluate, with guardrails (two abstention gates, always-on citations) that make
it safe to put in front of users. It runs fully offline with an optional LLM
layer. I'd love to apply the same grounded-workflow approach with your team.
Demo + code: [link].

## Backend Intern

Hi [Name] — applying for the Backend Intern role. I built a typed, validated
FastAPI service (9 endpoints, Pydantic, SQLAlchemy/SQLite, global error handling,
file validation) with 20 tests and GitHub Actions CI, containerized with
Docker Compose. It powers a retrieval-based document Q&A app. I care about clean,
tested, well-documented services — code: [link].

## ML / LLM Intern

Hi [Name] — applying for the ML/LLM Intern role. I built a retrieval pipeline
from scratch (tokenization, IDF weighting, length-normalized scoring, overlapping
chunking) and a focused extractive answerer with citations, plus an optional
OpenAI/Anthropic layer behind a strict grounding prompt. I evaluated it with a
golden dataset (100% citation coverage, 0 unsupported answers). Code + write-up:
[link].