All learning paths
Level 2Generative AI Engineering
GenAI Engineer Path
Master generative AI systems
Visual Roadmap
10 stages · click any stage to jump
L2 · Generative AI Engineering
1
LLMs
Foundations of large language models
2
Structured Outputs
JSON schemas & Pydantic contracts
3
Embeddings
Vector spaces & dense representations
4
Vector Search
Qdrant, pgvector & HNSW indexing
5
RAG
Hybrid search & reciprocal rank fusion
6
Multimodal
Vision models & audio processing
7
Agents
Tool calling & reasoning loops
8
Fine-Tuning
LoRA, QLoRA & instruction tuning
9
Evaluation
Ragas, TruLens & benchmarking
10
Production
vLLM, continuous batching & latency
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
- Fine-tune open weights models using LoRA and QLoRA