All learning paths
Level 2Generative AI Engineering
GenAI Engineer Path
Master generative AI systems
Visual Roadmap
11 stages · click any stage to jump
L2 · Generative AI Engineering
1
LLMs
Architecture, tokens, context windows
2
Structured Outputs
JSON schemas, validation, function calli…
3
Embeddings
Vector representations of meaning
4
Vector Search
ANN, indexing, similarity
5
RAG
Retrieval augmented generation
6
Multimodal
Vision, audio, cross-modal
7
Agents
Tool-using LLMs
8
Multimodal
Vision, audio, image generation
9
Fine-tuning
Customise model weights
10
Evaluation
Retrieval + generation metrics
11
Production
Reliability at scale
StartCapstone
Overview
Specialise in generative AI: LLMs, structured outputs, embeddings, vector search, RAG, multimodal systems, agents and production evaluation.
Learning outcomes
- Architect production RAG systems end-to-end
- Master embeddings, vector databases and retrieval strategies
- Build multimodal AI applications
- Implement structured output and function calling patterns
- Evaluate and improve generative AI quality
Stages
1
2
3
4
5
6
7
Agents
Tool-using LLMs
8
9
10
11
Production
Reliability at scale
Series in this path
Building LLM Applications
IntermediateTokens, context windows, prompting, structured outputs and function calling
LLM APIsPromptingStructured OutputsFunction Calling+2
3 chapters2 labs~8h
Production RAG
IntermediateBuild retrieval systems that actually work in production
RAGEmbeddingsVector SearchChunking+3
4 chapters2 labs~10h
LLM Evaluation
AdvancedMeasure retrieval, generation, agents and production quality
EvaluationRetrieval MetricsLLM-as-JudgeRegression Testing+2
3 chapters1 labs~7h
Building Multimodal Applications
AdvancedVision, audio, image generation and cross-modal AI systems
MultimodalVisionVLMSTT+3
3 chapters0 labs~7h