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12-week 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 4
Retrieval-augmented generation that holds up: chunking strategy, hybrid and contextual retrieval, reranking, and why retrieval quality dominates generation quality.
Bridges to Databases — query processing, joins, and information retrievalBuilds on: Embeddings & Vector Search
Week 5
Treat eval as engineering: golden datasets, LLM-as-judge with calibration, regression suites in CI, and catching silent quality drift before users do.
Bridges to Software Engineering — automated testing and continuous integrationBuilds on: Production RAG & Context Engineering
Week 6
An enterprise mandate: deliver a launched retrieval-augmented product over a large, real corpus that a non-technical team can trust for answers. Build the full system: ingestion and chunking, hybrid plus contextual retrieval with reranking, a grounded-and-cited generation layer, and — non-negotiable — an automated evaluation harness that scores retrieval and answer quality and runs in CI so quality regressions are caught before users see them. The deliverable must be production-grade and directly deployable: real public hosting, CI/CD, observability, security hardening against the OWASP LLM Top 10, and a hyper-usable, fast, accessible chat UI. It must be hyperscalable — the retrieval and serving layers hold as the corpus and traffic grow. And it ships complete with marketing: a landing page, a pitch, and a demo. A RAG demo is easy; a launched RAG product you can defend is the job. Ship it as a real product.
Why it matters: RAG is the default architecture for enterprise AI products, and the differentiator between teams is rigorous evaluation, not retrieval cleverness. A builder who ships a RAG platform with a CI-integrated eval harness is directly deployable as an AI Product Engineer or Applied AI Engineer, a ₹1-crore-tier role at companies betting their roadmap on grounded AI.
A publicly hosted RAG product with a stable URL and a fast, accessible chat UI, plus a public repo: the ingestion and retrieval pipeline, the citation-grounded answer layer, an automated eval harness with a golden dataset wired into CI, CI/CD on every commit, observability of cost and latency, a marketing landing page, a 10-slide pitch, a recorded demo, and a README documenting the retrieval, evaluation, security, and scaling design.
Market signal — who wants thisProduction RAG is a mature, funded 2026 market, and its defining requirement is rigorous evaluation: systematic eval frameworks (context precision, context recall, faithfulness, answer relevancy) are now mandatory for enterprise deployments, served by RAGAS, Galileo, Braintrust, and Maxim AI. Enterprise knowledge systems are forecast to keep evolving hard through 2026-2030. Investors fund RAG platforms with built-in eval because retrieval cleverness is commoditized; trustworthy, regression-proof answer quality is what enterprises actually pay for.
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: LLM Evaluation 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
Run AI in production sustainably: prompt and model versioning, A/B rollout, caching, fallback chains, rate limiting, and a cost model you can defend to finance.
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
An enterprise mandate: the company's AI features are live and the security and reliability teams are flying blind. Build and launch a product with two interlocking systems: an observability pipeline that traces every LLM call with token, cost, and latency telemetry and surfaces silent quality drift; and an automated red-team harness that continuously attacks the AI product with prompt injection, jailbreaks, and data-exfiltration probes, and reports which guardrails held. The deliverable is directly deployable and hyperscalable: real public hosting, CI/CD, a hyper-usable dashboard a security lead reads at a glance, the platform itself secured, and the ingestion path able to absorb high call volume. It ships complete with marketing — a landing page, a pitch, and a demo. Deliver something an enterprise can buy and run on a real product before an incident, not after. Ship it as a real product.
Why it matters: AI security and observability is a board-level concern as AI features ship into regulated industries, and almost no one combines both. A builder who delivers a tracing-plus-red-team pipeline is directly deployable as an AI Security Engineer or LLMOps Lead, a scarce ₹1-crore-tier role because it sits at the intersection of security, reliability, and AI.
A publicly hosted product with a stable URL and a hyper-usable security dashboard, plus a public repo: the tracing and observability pipeline, the automated red-team attack suite with a results report, the guardrails it validates, CI/CD on every commit, a marketing landing page, a 10-slide pitch, a recorded demo, and a README documenting the threat model, the drift-detection method, and the scaling design.
Market signal — who wants thisAI security is a proven, acquisition-grade 2026 market: Lakera, which built exactly this guardrails-plus-red-teaming product (Lakera Guard at 98%+ detection, sub-50ms; Lakera Red for automated attack simulation), was acquired by Cisco in May 2025 and folded into Cisco AI Defense. Evaluation leaders like Galileo now ship guardrails that intercept outputs before tool execution. Investors fund AI observability and red-teaming because shipping AI into regulated industries makes pre-incident security a board-level requirement, and almost no product combines tracing and red-teaming in one.