Academy · Engineering
AI Engineering
Build production AI systems. Master LLM integration, RAG architectures, fine-tuning, vector databases, and deployment at scale — the engineering skills that separate prototypes from production.
What is the Engineering path?
The Engineering path is designed for software engineers, ML practitioners, and technical founders who are building AI systems. It goes deep on the practical engineering challenges: how to integrate LLM APIs reliably, how to build RAG systems that actually retrieve relevant content, when to fine-tune vs. use RAG, how to choose and configure vector databases, and how to deploy everything with proper observability, caching, and fallback strategies.
This path assumes comfort with Python or TypeScript, basic API development, and general software engineering principles. You should understand what an LLM is before starting — the Foundations path or equivalent knowledge is a prerequisite.
After completing this path you'll be able to...
- ✓Integrate LLM APIs with streaming, function calling, and proper error handling
- ✓Build end-to-end RAG systems — chunking, embedding, retrieval, reranking, and evaluation
- ✓Choose the right vector database for your use case and configure it properly
- ✓Decide when to fine-tune vs. use RAG vs. prompt engineering — and implement the choice
- ✓Deploy AI systems with caching, rate limiting, monitoring, and fallback strategies
- ✓Build evaluation pipelines that measure quality, not just latency
- ✓Handle production concerns — cost optimization, PII filtering, and prompt injection defense
Curriculum preview
Working with OpenAI, Anthropic, and open-source models — streaming, function calling, and rate limits.
Building retrieval pipelines — chunking, embeddings, vector stores, reranking, and evaluation.
When and how to fine-tune — LoRA, QLoRA, instruction tuning, and when RAG is better.
Serving at scale — latency, caching, monitoring, evals, and incident response.
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