Guided path · intermediate
Applied AI Engineering & Retrieval
Implement Retrieval-Augmented Generation (RAG), vector stores, automated trajectory evaluation, and cost governance.
4 modules160 minutesApplied AI Engineer badge
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Complete the path to earnApplied AI EngineerWho this is for
Practitioners designing robust retrieval pipelines, evaluation harnesses, and token economics.
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Modules
- 0135 min · intermediateRetrieval-augmented generation, embeddings, and vector searchThis intermediate module teaches you to design and evaluate a retrieval-augmented generation pipeline. After completing it, you can explain how embeddings and vector search connect a question to relevant source text, choose between exact and approximate search, and diagnose retrieval separately from answer generation.Begin →
- 0240 min · intermediateEmbeddings and vector storesThis intermediate module teaches you to design an embedding-based retrieval workflow, choose between exact and approximate search, and evaluate whether retrieved records are useful. After completing it, you can describe and troubleshoot the path from source content to vectors, stored records, similarity search, and retrieved context.Begin →
- 0340 min · intermediateAgent evaluation and trajectory scoringThis intermediate module teaches you to design an agent evaluation that separates task outcomes from trajectory quality. After completing it, you can define a scoring rubric, calculate a normalized trajectory score, handle invalid trials, and interpret results without confusing plausible behavior with verified success.Begin →
- 0445 min · intermediateCost, capacity, and spend control for AI systemsThis intermediate module teaches you to calculate unit cost, translate measured demand into capacity, forecast spend, and choose controls that balance financial limits with service reliability. After completing it, you can build and explain a provider-neutral cost and capacity plan instead of treating an invoice or budget as an unexplained total.Begin →