N004

Reliable AI Applications with LLMs & RAG

Build the ability to design reliable, traceable, and evaluable AI solutions by combining large language models with Retrieval-Augmented Generation architectures.

Practitioner3 days

Training profile

Target audience

Software teams building AI products, data and ML professionals, product managers, and AI project leads.

Outcomes

Framing LLM use cases and RAG requirements correctly
Designing ingestion, chunking, embedding, and vector search flows
Measuring answer quality, hallucination, and citation behavior
Designing security, privacy, and human oversight controls

Curriculum

Transformer and LLM fundamentals
RAG architecture: ingestion, chunking, embeddings, and retrieval
Hybrid search, reranking, and context-window design
Prompt templates, tool use, and structured outputs
RAG evaluation: correctness, faithfulness, relevance, and latency
Hallucination reduction, citations, and security
Productionization, observability, and cost optimization