Knowledge Graphs & GraphRAG
Go beyond vector search: model enterprise knowledge as a graph, extract entities and relations with LLMs, and combine graph traversal with retrieval for multi-hop questions plain RAG gets wrong. Learners benchmark GraphRAG against vanilla RAG on the same question set.
What this course covers.
5 modules, 32 named skill atoms. Expand any module to see them.
1When Vectors Aren't Enough6 skill atoms
2Graph Modeling7 skill atoms
3LLM-Powered Extraction7 skill atoms
4GraphRAG Patterns6 skill atoms
5Head-to-Head Benchmark6 skill atoms
AI-303 in a role journey.
This course appears in 1 of our 45 role journeys. Here is what a learner takes immediately before and after it in each.
Roles this course serves
This course is authored to band B5.
Every course we run is written to one rung of the CASI ladder, so a plan can be assembled to take a team from where they are to where they need to be.
What do B1–B6 mean?The CASI Capability Ladder — click to expand
Every course targets a band on the CASI Capability Ladder — our six-band proficiency scale, anchored to open standards (O*NET, ESCO, NICE, NIST AI RMF, Bloom's). A band tells you how deep a course goes, and what evidence proves it.
A note on B6. Courses in this catalog target B1–B5. B6 is not taught — it is recognised, through a portfolio and a panel, once someone is setting direction for others. Every journey here is built to land a learner at B5.
Other AI courses at this level.
Model Compression: Quantization, PEFT, QLoRA & Distillation
GPU Engineering for AI (CUDA · cuDNN · TensorRT)
Ray & Large-Scale Model Training
Preference Tuning: RLHF, DPO & Reward Models
Multi-Agent Systems & Orchestration
LLM Inference Optimization & Serving (vLLM · LiteLLM)
Run AI-303 for your team.
This course runs at several lengths depending on how deep you need to go and how much of it your people already have. Tell us who is being trained and we will scope it.