LLM Integration
Vector Retrieval Production Patterns
Sharding, quantization trade-offs, and evaluation harnesses for semantic search that survives traffic spikes.
- Duration
- 4 weeks · hybrid
- Format
- Cohort + labs
- Level
- Intermediate
- Tuition (informational)
- ₩980,000
Program narrative
You will implement hybrid sparse+dense retrieval, staged rollouts, and offline replay sets grounded in your domain language. Governance modules cover PII redaction in embeddings and audit logging for query prompts.
What is included
- · ANN parameter matrix with recall@k reporting
- · Canary routing recipes for index swaps
- · Synthetic load generator with documented skew
- · PII scanning hooks before embedding writes
- · Latency SLO worksheet tied to autoscaling policies
- · Mentor review of your evaluation rubric
- · Postmortem template for index corruption events
Outcomes you can demo
- · Publish a hybrid retrieval design others can extend
- · Run a statistically sound offline eval comparing two indexes
- · Document rollback steps that do not require a full reindex
Mentor of record
Elias Cho
Search infra lead for multilingual commerce; publishes on responsible recall metrics.
Participant questions
Which vector databases are covered?
We teach patterns portable across FAISS, managed ANN, and OpenSearch k-NN. Vendor-specific autotuning is out of scope.
Do you provide corpora?
We supply a sanitized public corpus and encourage you to bring a small internal slice under NDA rules you confirm in writing.
Known limitation?
Cross-region replication strategies for indexes are overview only; full multi-region playbooks are not included.
Recent participant notes
“Vector Retrieval Production Patterns gave us a canary recipe we reused for two quarterly index bumps without user-visible drift.”
“Hybrid sparse+dense week was the standout. Would like a deeper tangent on tokenization for Korean product titles, but mentors responded async.”