docs/PRESENTATION_SCRIPT.md

Presentation Script

One-line Pitch

I built a grounded QA system over official technical documentation with reproducible artifact generation, benchmark evaluation, and terminal-first usage for both factoid and explanatory questions.

What It Does

• crawls official docs
• converts them into searchable chunks
• retrieves evidence with sparse and dense search
• fine-tunes the dense retriever on project-generated domain pairs
• reranks passages
• extracts answers with a QA reader
• synthesizes grounded explanatory answers from multiple supporting chunks
• evaluates quality with benchmark metrics

Why It Is Not Just a Demo

• it has a real artifact pipeline
• it stores local model snapshots and indexes
• it includes benchmark evaluation and error analysis
• it has deterministic rebuild behavior
• it has CI and tests

Suggested Live Flow

1. Show scripts/qa_cli.py status
2. Show scripts/qa_cli.py ask "Which parameter type can you declare in a FastAPI path operation to set response headers?"
3. Show scripts/qa_cli.py ask --style explanatory "How do you set custom response headers in FastAPI, and why does using a Response parameter work?"
4. Show scripts/qa_cli.py eval --threshold 0.0
5. Open artifacts/real_qa/reports/evaluation_report.md
6. Open artifacts/real_qa/reports/error_analysis.md

Honest Framing

• this is a serious QA system, not a novelty LLM product
• the main strength is end-to-end retrieval QA engineering with grounded explanatory synthesis
• the next scaling step would be larger supervised training and a larger benchmark
• current snapshot is already reproducible and benchmarked, not just a notebook demo