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Level 2Generative AI Engineering

GenAI Engineer Path

Master generative AI systems

Visual Roadmap

9 stages · click any stage to jump

L2 · Generative AI Engineering
1

LLMs

How models work, what they cost, and how…

2

Structured Outputs

Schemas, validation, function calling an…

3

Embeddings & Vector Search

Represent meaning as vectors, index them…

4

RAG

Retrieval-augmented generation from inge…

5

Multimodal

Vision, audio and image generation.

6

Agents

Tool-using models and agent loops.

7

Fine-tuning

When to change a model's weights, and wh…

8

Evaluation

Retrieval and generation metrics, judges…

9

Production

Reliability, cost, context engineering a…

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
LLMs0%

How models work, what they cost, and how to choose one.

Building LLM Applications
2
Structured Outputs0%

Schemas, validation, function calling and streaming.

Building LLM Applications
3
Embeddings & Vector Search0%

Represent meaning as vectors, index them and search them well.

Production RAG
4
RAG0%

Retrieval-augmented generation from ingestion to reranking.

Production RAG
5
Multimodal0%

Vision, audio and image generation.

Building Multimodal Applications
6
Agents

Tool-using models and agent loops.

7
Fine-tuning0%

When to change a model's weights, and when not to.

Building LLM Applications
8
Evaluation0%

Retrieval and generation metrics, judges and regression testing.

LLM Evaluation
9
Production

Reliability, cost, context engineering and content safety at scale.

Series in this path

Building LLM Applications
Intermediate
Tokens, context windows, prompting, structured outputs and function calling
LLM APIsPromptingStructured OutputsFunction Calling+2
3 chapters2 labs~8h
Production RAG
Intermediate
Build retrieval systems that actually work in production
RAGEmbeddingsVector SearchChunking+3
5 chapters2 labs~11h
LLM Evaluation
Advanced
Measure retrieval, generation, agents and production quality
EvaluationRetrieval MetricsLLM-as-JudgeRegression Testing+3
4 chapters1 labs~8h
Building Multimodal Applications
Advanced
Vision, audio, image generation and cross-modal AI systems
MultimodalVisionVLMSTT+3
3 chapters0 labs~7h