CASE STUDY / 01
Semantic discovery at streaming scale
Help people discover relevant content as a large catalog changes.
Responsibility
The résumé describes leading the RAG architecture decision, implementing semantic search with FAISS and Pinecone, and building inference pipelines with FastAPI, Docker and Kubernetes.
Architecture at a glance
- Content metadata
- Vector retrieval
- Grounded generation
- Recommendations
Reported outcome
+30% content discovery accuracy
The PDF reports an improvement in content discovery accuracy. It does not define the relevance metric or say whether this is relative change or percentage points.
Baseline, sample size, evaluation window and experiment design are not published. The 200M+ audience figure is résumé-reported scale context; measured reach attributable to this system is not established here.
Read the source résumé (PDF)Technical deep dive
Design decision
RAG keeps changing catalog knowledge in retrieval and balances scalability with operating cost.
Alternatives and tradeoffs
End-to-end fine-tuning can specialize model behavior, but catalog updates also require a plan for refreshing knowledge. Retrieval adds its own indexing and relevance failure modes.
Engineering takeaway
Evaluate retrieval relevance separately from generation quality. A grounded answer can still be unhelpful if retrieval misses the right content.