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Certification

Certification in Building and Deploying RAG LLM Pipelines with LangChain

Become certified in RAG with LangChain. Prove you can build and ship a production-ready LLM pipeline: data ingestion, chunking, embeddings, Chroma/FAISS retrieval, prompt design, eval, and citations. Deliver reliable, citable answers at scale.

Exam of 10 to 20 questionsCertificate for LinkedInIntermediate

The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

What the exam covers

2 questions from each of the 15 chapters of the course

20 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01What RAG is8:14
  2. 02The RAG pipeline8:59
  3. 03Technical steps and plan4:09
  4. 04Ingestion theory and setup9:01
  5. 05Document structure8:02
  6. 06Text loading5:11
  7. 07PDF loading6:54
  8. 08Chunking and metadata5:35
  9. 09Vector database theory5:52
  10. 10Implementing embeddings6:50
  11. 11Vector store implementation10:12
  12. 12Retrieval pipeline11:23
  13. 13LLM integration10:10
  14. 14Advanced RAG6:15
  15. 15Modular RAG application21:22
About the course

RAG with LangChain: Build an LLM Pipeline in 2 Hours (Video Course) is a focused certification that takes you from idea to a production-ready retrieval-augmented generation system. You'll master the essentials,ingestion, chunking, embeddings, retrieval, Chroma/FAISS, prompts, and evaluation,so you ship clean LangChain code faster, make better decisions with citations, and stay competitive in a fast-moving field. Enroll today to get a practical toolkit you can apply the same day and build with confidence in under two hours.

This certification covers the following topics:

  • RAG architecture: ingestion pipeline and retrieval/generation pipeline
  • Document ingestion: loaders, cleaning, normalization, and metadata tagging
  • Chunking strategies and windowing for higher-quality recall
  • Embedding model selection and configuration
  • Vector stores in practice: Chroma vs FAISS, indexing, filtering, and updates
  • Prompt design that reduces hallucinations, with citations and confidence scores
  • Advanced retrieval techniques: hybrid search, reranking, and query expansion
  • Clean, modular LangChain code structure and components
  • Evaluation and monitoring: quality checks, regression tests, and guardrails
  • Scaling, performance tuning, and cost optimization
  • Security, privacy, and access control for RAG systems
  • From prototype to production: deployment patterns and common pitfalls

Jobs this certification suits

Our AI checked this certification against 500 jobs; these get the most out of it. Each job links to its learning path.