Data and AI

Building LLM Applications with RAG

Ground large language model applications in genuine, current, private data using retrieval-augmented generation

20h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You can build a working RAG pipeline (chunking, embedding, retrieval, generation), understand the genuine trade-offs in each pipeline stage, and evaluate whether a RAG system's answers are actually grounded in retrieved content rather than hallucinated.

Job titles this qualifies you for

LLM EngineerAI EngineerMachine Learning Engineer
Market intelligence
Demand
High
Remote
70% remote roles
Nigeria salary
Not a primary local hiring category
Remote (USD)
$101,500–$155,000/year for the typical remote range (25th-75th percentile); averages reported between $128,769 and $195,475/year depending on source; specialised skills (AI agent architecture, LLM fine-tuning) can increase pay up to 25%

Lessons

Stage 1 — Foundation
1
Why RAG: The Genuine Limits of LLM Training Data
30 min
2
Chunking and Embedding Documents
40 min
Stage 2 — Applied
1
Retrieval and Generation: The Full RAG Pipeline
40 min
2
Evaluating RAG Systems and Reducing Hallucination
30 min
Tier 2 Proof Submission

Building LLM Applications with RAG Portfolio

Demonstrate genuine understanding of RAG's purpose, chunking/embedding trade-offs, the full retrieval-generation pipeline, and evaluation/hallucination-reduction discipline.

1.A knowledge-cutoff explanation and a RAG-versus-fine-tuning scenario
2.A chunking trade-off explanation with a concrete context-loss example
3.A full RAG pipeline description including no-relevant-information handling
4.A retrieval-versus-faithfulness distinction and a test set question
Create a free account to join this programJoin free →