What you will gain
- Move from prompt to a real architecture
- Understand retrieval
- Instrument limitations
Target outcomes
- Integrate a model into an application
- Design a simple RAG flow
- Set up technical guardrails
Prerequisites
- JavaScript or Python basics
- Backend web knowledge
Establish a sound architecture foundation
We cover the essential building blocks of an LLM application: model choice, system prompt, context handling, costs, and integration limits.
- Model APIs
- System prompting
- Observability
Build a usable RAG pipeline
The training shows how to prepare a corpus, split documents, query a vector database, and return a more reliable answer.
- Chunking
- Retrieval
- Sourced answers
Move from prototype to product component
We conclude with the quality, security, and monitoring challenges that are necessary to integrate these building blocks sustainably into a product.
- Fallbacks
- Business logs
- Costs and limits
IT
Instructor
Imane Tahiri
LLM Engineer
This training is designed to quickly turn understanding into execution capacity, with a clear, concrete and directly applicable format.
