AIwithTS
Coming SoonintermediateQ4 2026

RAG Engineering for Production: From Simple Context to Advanced Pipelines

From simple context injection to production RAG pipelines. Learn every strategy for making AI know things — and how to pick the right one.

4 modules21 lessons~45 hours

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Prerequisites

  • -Comfortable with TypeScript
  • -Basic understanding of vector databases or embeddings (helpful but not required)
  • -Familiarity with at least one LLM API

What You'll Learn

Master every retrieval-augmented generation strategy, from basic context injection to advanced multi-stage pipelines. Learn when to use each approach and how to evaluate what works.

Module 1: Foundations

Start with naive RAG. Document loading, text chunking strategies, embedding models, and vector search. Build a working RAG pipeline from scratch.

Module 2: Advanced Retrieval

Go beyond basic similarity search. Hybrid search, reranking, query transformation, hypothetical document embeddings (HyDE), and multi-query retrieval.

Module 3: Pipeline Architecture

Design production RAG systems. Routing, fallback strategies, multi-index retrieval, metadata filtering, and agentic RAG with tool-based retrieval.

Module 4: Evaluation & Production

Measure and improve your RAG system. Automated evaluation with RAGAS, A/B testing, monitoring retrieval quality, caching, and cost optimization.

Who This Course Is For

  • AI engineers building knowledge-grounded applications
  • Backend developers adding RAG capabilities to existing products
  • TypeScript developers who want a systematic approach to retrieval-augmented generation
  • Teams evaluating and improving their RAG pipelines
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