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Elite track
Ship AI products that survive real users and real attackers.
Build the full product around a model: retrieval and context engineering at scale, LLM evaluation and observability, AI red-teaming and security, cost governance, and LLMOps. You leave able to take an AI feature from prototype to a hardened, monitored, publicly hosted product. Bridges to Databases, Software Engineering, and Information Security.
Week by week
Every week unlocks the next. Concepts route you to free, world-class material; projects turn that knowledge into something deployed.
Week 1
The anatomy of a production LLM feature: the model call, structured output, streaming, error handling, and the latency and cost budget you design against from day one.
Bridges to Software Engineering — application architecture and API designBuilds on: nothing, start here
Week 2
Represent meaning as vectors and search it fast: embedding models, similarity metrics, approximate nearest neighbor (HNSW), and the recall-versus-latency tradeoff.
Bridges to Databases — indexing, search structures, and query optimizationBuilds on: LLM Application Foundations
Week 3
Master prompt cache ergonomics, KV-cache reuse windows, and prefix breakpointing across frontier APIs. Inspect cache-miss diagnostics to eliminate cache churn, optimize reuse windows, and minimize latency and serving costs.
Bridges to Databases — API/schema versioning, semantic routing, and transactional isolationBuilds on: Embeddings & Vector Search
Week 4
Architect modern retrieval pipelines moving beyond naive chunk-and-embed methods to asymmetric late-interaction token scoring (ColBERTv2), contextual BM25 hybrid ranking, and prompt-cached prefix compression.
Bridges to Databases — query processing, joins, and information retrievalBuilds on: Embeddings & Vector Search
Week 5
Build rigorous evaluation-driven development (EDD) pipelines, synthetic test-case generation, automated unit assertions, and CI regression suites for production AI applications.
Bridges to Software Engineering — automated testing and continuous integrationBuilds on: Production RAG: Asymmetric Late Interaction & Contextual Retrieval
Week 6
Build a production-ready RAG platform backed by an agent-first embedded vector lakehouse (LanceDB/pgvector). Implement an automated EDDOps validation suite with custom golden datasets, trace-based latency checks, and LLM-as-a-judge regression tests.
A dockerized, query-active vector service and structured context database with live tracing and a production CI/CD evaluation gate.
Week 7
See inside a non-deterministic system: distributed tracing of LLM calls, token and cost metrics, latency percentiles, and structured logging with correlation IDs.
Bridges to Software Engineering — monitoring, logging, and distributed tracingBuilds on: Evaluation-Driven Development (EDD) & Synthetic Data Harnesses
Week 9
Attack your own product before someone else does: prompt injection, jailbreaks, data exfiltration, PII leakage, and building input/output guardrails that actually hold.
Bridges to Information Security — threat modeling, penetration testing, and OWASPBuilds on: AI Observability & Tracing
Week 10
Operationalize model deployments with high availability and resilience. Design dynamic, multi-cloud and multi-model fallback topologies (routing between cloud APIs and local open-weight fallbacks) to mitigate geopolitical risk, single-provider outages, and sudden regulatory export controls.
Bridges to Software Engineering — release management, versioning, and CI/CDBuilds on: AI Security & Red-Teaming
Week 12
Take an AI feature to a real public launch: containerized deploy, health checks, graceful degradation when the model is slow or down, and a launch story that sells the experience.
Bridges to Software Engineering — deployment, resilience, and product engineeringBuilds on: LLMOps, Cost Governance & Geopolitical Fallbacks
Build a production AI observability and safety defense pipeline featuring OpenTelemetry cache diagnostics, automated red-teaming for dual-use exploits, and a hardware/software kill-switch API.
Why it matters: Production governance and security platforms for frontier AI systems under strict regulatory oversight.
A hardened monitoring and governance gateway with granular cache telemetry, automated multi-turn jailbreak/exploit test suites, and cryptographic emergency halt controls.
What's next
Each track stands alone, so there's no wrong order. If you want a suggestion, this one pairs well next.