Learning roadmap
A sequenced guide through the skills, tracks, and practice materials you need to become an effective production AI engineer — not just an LLM API caller.
Before diving into RAG pipelines or agent architectures, every AI engineer needs a solid foundation: embeddings, APIs, prompt design, vector search, and basic evaluation. This phase is where the mental model forms.
Core concepts every AI engineer needs before building production systems. Start here to build a solid mental model of embeddings, APIs, prompts, vectors, evaluation, and basic deployment.
Choose your specialization path. RAG engineers build retrieval pipelines, handle chunking tradeoffs, and debug hallucinations. Agentic AI engineers design reliable tool-calling agents with planning, memory, and human review.
Build, evaluate, debug, and deploy real-world retrieval-augmented generation systems. Go beyond toy examples to handle chunking tradeoffs, hybrid retrieval, reranking, citation grounding, and RAG observability.
Design reliable AI agents with tools, memory, planning, guardrails, and human review workflows. Learn the patterns used in production agentic systems, not just demos.
Shipping is not the finish line. LLMOps engineers instrument tracing, monitor costs and latency, build evaluation pipelines, run regression tests before prompt changes, and manage production incidents.
Operate AI systems with tracing, evaluation pipelines, regression tests, cost monitoring, and production dashboards. Learn the engineering discipline behind reliable LLM products.
At the senior level, AI engineers design full systems, articulate tradeoffs clearly, and build monetizable AI products. These tracks prepare you for staff-level system design interviews and AI SaaS product development.
Prepare for senior AI engineering interviews with system design walkthroughs, architectural tradeoffs, metric definitions, failure mode analysis, and mock interview practice.
Build monetizable AI products with authentication, payments, SEO, analytics, and AI features. Learn the full-stack AI SaaS architecture used in production.
Weekly newsletter
Weekly guides, interview prep, architecture breakdowns, and production lessons for engineers building with AI — free forever.