AI · SAAS · MISSION 01 Demo data2 MIN · 4 CHAPTERS · 1 FIGURE
RAG Pipeline
A retrieval system that turns [N] PDFs into cited answers in [X.X] seconds for [CLIENT]’s support team.
- P95 latency
- [X.X] s
- Answer accuracy
- [XX] %
- Corpus
- [N] PDFs
- Stack
- Python · pgvector · Node · Next.js
- Year
- [YEAR]
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Chapters 04
- 01The ProblemSupport was losing [N] minutes per ticket searching [N] PDFs for the one paragraph that held the answer.
- 02The BuildIngest → chunk → embed → hybrid retrieve → rerank → generate, with a nightly reindex and an eval run on every deploy.
- 03The Hard PartThe constraint that failed first, how the eval set found it, and how it was fixed.
- 04ResultsP95 [X.X] s from question to first words, and [XX] % accuracy on the eval set.