AI Data Systems
Build reliable data, feature, embedding, retrieval, evaluation, and observability pipelines for AI systems.
#LessonPhaseTimeRequirementOpen
01Data ManagementData is the fuel. How you manage it determines how fast you go.Phase 0045 minRequired▸ Visual story02Feature Engineering & SelectionA good feature is worth a thousand data points.Phase 0290 minRequired▸ Visual story03ML PipelinesA model is not a product. A pipeline is. The pipeline is everything from raw data to deployed prediction, and every step must be reproducible.Phase 02120 minRequired▸ Visual story04Data Pipelines for Pre-TrainingThe model is a mirror. It reflects whatever data you feed it. Feed it garbage, it reflects garbage with perfect fluency.Phase 1090 minRequired↗ Official lesson05Embeddings & Vector RepresentationsText is discrete. Math is continuous. Every time you ask an LLM to find "similar" documents, compare meanings, or search beyond keywords, you're relying on a bridge between these two worlds. That bridge is an embedding. If you don't understand embeddings, you don't understand modern AI. You just use it.Phase 1175 minRequired↗ Official lesson06Context Engineering: Windows, Budgets, Memory, and RetrievalPrompt engineering is a subset. Context engineering is the whole game. A prompt is a string you type. Context is everything that goes into the model's window: system instructions, retrieved documents, tool definitions, conversation history, few-shot examples, and the prompt itself. The best AI engineers in 2026 are context engineers. They decide what goes in, what stays out, and in what order.Phase 1190 minRequired↗ Official lesson07RAG (Retrieval-Augmented Generation)Your LLM knows everything up to its training cutoff. It knows nothing about your company's docs, your codebase, or last week's meeting notes. RAG solves this by retrieving relevant documents and stuffing them into the prompt. It's the most deployed pattern in production AI. If you build one thing from this course, build a RAG pipeline.Phase 1190 minRequired↗ Official lesson08Advanced RAG (Chunking, Reranking, Hybrid Search)Basic RAG retrieves the top-k most similar chunks. That works for simple questions. It falls apart for multi-hop reasoning, ambiguous queries, and large corpora. Advanced RAG is the difference between a demo that works on 10 documents and a system that works on 10 million.Phase 1190 minRequired↗ Official lesson09Evaluation & Testing LLM ApplicationsYou would never deploy a web app without tests. You would never ship a database migration without a rollback plan. But right now, most teams ship LLM applications by reading 10 outputs and saying "yeah, looks good." That is not evaluation. That is hope. Hope is not an engineering practice. Every prompt change, every model swap, every temperature tweak changes your output distribution in ways you cannot predict by reading a handful of examples. Evaluation is the only thing standing between your application and silent degradation.Phase 1145 minRequired↗ Official lesson10Building a Production LLM ApplicationYou have built prompts, embeddings, RAG pipelines, function calling, caching layers, and guardrails. Separately. In isolation. Like practicing guitar scales without ever playing a song. This lesson is the song. You will wire every component from Lessons 01-12 into a single production-ready service. Not a toy. Not a demo. A system that handles real traffic, fails gracefully, streams tokens, tracks costs, and survives its first 10,000 users.Phase 11120 minRequired↗ Official lesson11LLM Observability Stack SelectionThe 2026 observability market splits into two categories. Development platforms (LangSmith, Langfuse, Comet Opik) bundle monitoring with evals, prompt management, session replays. Gateway/instrumentation tools (Helicone, SigNoz, OpenLLMetry, Phoenix) focus on telemetry. Langfuse is MIT-licensed core with strong OSS balance (50K events/month free cloud). Phoenix is OpenTelemetry-native under Elastic License 2.0 — excellent for drift/RAG visualization, not a persistent production backend. Arize AX uses zero-copy Iceberg/Parquet integration claiming 100x cheaper than monolithic observability. LangSmith leads for LangChain/LangGraph, $39/user/mo, self-host in Enterprise only. Helicone is proxy-based with 15-30 min setup, 100K req/mo free, but less depth on agent traces. Common production pattern: Gateway (Helicone/Portkey) + eval platform (Phoenix/TruLens) glued by OpenTelemetry.Phase 1760 minRequired↗ Official lesson