Generative AI Engineer Roadmap 2026
Specialise in generative AI: LLMs, structured outputs, embeddings, vector search, RAG, multimodal systems, agents and production evaluation.
Visual Roadmap
9 stages · click any stage to jump
LLMs
How models work, what they cost, and how…
Structured Outputs
Schemas, validation, function calling an…
Embeddings & Vector Search
Represent meaning as vectors, index them…
RAG
Retrieval-augmented generation from inge…
Multimodal
Vision, audio and image generation.
Agents
Tool-using models and agent loops.
Fine-tuning
When to change a model's weights, and wh…
Evaluation
Retrieval and generation metrics, judges…
Production
Reliability, cost, context engineering a…
Stage by stage
0/22 chapters read · 0%1LLMs1 week0/2 readHow models work, what they cost, and how to choose one.
You will be able to
- Explain tokens, context windows, sampling and their effect on cost
- Compare hosted and open models on quality, latency and cost for your task
- Run an open model locally
Ready to move on when: You have compared three models on your own task, and can justify which one you would ship and why.
2Structured Outputs1 week0/2 readSchemas, validation, function calling and streaming.
You will be able to
- Get schema-valid JSON from any model, and repair it when it fails
- Stream partial results without breaking the structure
- Design prompts as versioned, testable artefacts
Ready to move on when: Your extraction pipeline returns valid structured output on 100% of a 50-document test set.
3Embeddings & Vector Search2 weeks0/1 readRepresent meaning as vectors, index them and search them well.
You will be able to
- Explain what embeddings capture, and what they miss
- Choose an embedding model and measure it on your own data
- Index vectors and tune approximate nearest-neighbour search
- Combine keyword and vector search with hybrid retrieval
- Plan for re-embedding when the model changes
Full series: Production RAGPractise
Ready to move on when: You can show recall@10 for keyword, vector and hybrid search on a labelled query set, and explain the differences.
4RAG3 weeks0/4 readRetrieval-augmented generation from ingestion to reranking.
You will be able to
- Ingest PDFs, HTML and tables without losing structure
- Pick a chunking strategy using evidence, not defaults
- Rerank for precision, and cite sources in every answer
- Diagnose whether a bad answer is a retrieval or a generation failure
- Defend against instructions hidden in retrieved documents
Full series: Production RAGPractise
- LabDocument Chunking Strategies
- LabReranking for Retrieval Precision
- Debug challengeGood Retrieval, Bad Answers
- Debug challengeInjection via Retrieved Documents
- ProjectRAG Assistant
- Interview questionDesign a RAG system for a bank with 60,000 internal documents and 18,000 employees with role-based access control.
Ready to move on when: For any wrong answer, you can show whether retrieval or generation failed, and fix it.
5Multimodal2 weeks0/3 readVision, audio and image generation.
You will be able to
- Extract information from images, charts and scanned documents
- Build speech-to-text and text-to-speech pipelines
- Generate and edit images, and know when the output is unreliable
Ready to move on when: Your system answers questions about charts and scanned pages, and you have measured where it misreads them.
6Agents1 week0/2 readTool-using models and agent loops.
You will be able to
- Give a model tools with precise schemas
- Build an agent loop with stopping conditions
- Recover from tool errors without spiralling
Ready to move on when: Your agent uses two tools to complete a task, and stops cleanly when a tool keeps failing.
7Fine-tuning1 week0/1 readWhen to change a model's weights, and when not to.
You will be able to
- Decide between prompting, RAG and fine-tuning for a given problem
- Prepare a clean training set and hold out an evaluation set
- Run a parameter-efficient fine-tune and compare it against the base model
Ready to move on when: You can show, with an evaluation, whether fine-tuning beat a well-prompted base model on your task.
8Evaluation2 weeks0/3 readRetrieval and generation metrics, judges and regression testing.
You will be able to
- Score retrieval and generation separately
- Calibrate LLM judges against human labels
- Gate releases on evaluation results
Full series: LLM EvaluationPractise
Ready to move on when: A model upgrade runs your evaluation automatically, and you can say whether it is safe to ship.
9Production1 week0/4 readReliability, cost, context engineering and content safety at scale.
You will be able to
- Trace cost, latency and quality per request
- Route requests to the cheapest model that is good enough
- Cache safely, and moderate inputs and outputs
Ready to move on when: You know your cost per answer and your p95 latency, and have cut one of them without lowering quality.
Capstone: A multimodal document question-answering system
Build a system that answers questions over a set of real reports, including their tables, charts and scanned pages. Compare a baseline RAG pipeline with your improved one, and decide with evidence whether fine-tuning is worth it.
What to deliver
- A repository and README that runs ingestion and the question-answering API
- An evaluation set of at least 50 questions, a third of them about tables or charts
- Baseline and improved scores for retrieval and for answers, side by side
- A one-page decision on fine-tuning, backed by an experiment
- Cost per answer and p95 latency for the final system
What strong work looks like
- Retrieval quality
- Recall improves measurably from baseline, and the reasons are explained.
- Multimodal handling
- Table and chart questions are answered correctly, and misreads are measured.
- Evaluation rigour
- Retrieval and generation are scored separately, with calibrated judges.
- Engineering
- Structured outputs are always valid; ingestion is repeatable and versioned.
- Decision quality
- The fine-tuning decision follows from the data, including a 'no' if that is what it shows.
Start from: Enterprise Knowledge Assistant (project) · Hybrid Search (pattern)
What you'll be able to do
Series in this roadmap
Building LLM Applications
Tokens, context windows, prompting, structured outputs and function calling
Production RAG
Build retrieval systems that actually work in production
LLM Evaluation
Measure retrieval, generation, agents and production quality
Building Multimodal Applications
Vision, audio, image generation and cross-modal AI systems
Generative AI Engineer roadmap FAQ
How long does it take to become a Generative AI Engineer?
About 14 weeks of focused study on this roadmap, which has 9 stages. Engineers with software fundamentals who want to specialise in building generative AI applications.
What does the Generative AI Engineer roadmap cover?
In order: LLMs, Structured Outputs, Embeddings & Vector Search, RAG, Multimodal, Agents, Fine-tuning, Evaluation, Production.
What will I be able to do after the Generative AI Engineer roadmap?
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.
How should I split my time on the Generative AI Engineer roadmap?
At 8 to 10 hours a week: LLMs 1 week, Structured Outputs 1 week, Embeddings & Vector Search 2 weeks, RAG 3 weeks, Multimodal 2 weeks, Agents 1 week, Fine-tuning 1 week, Evaluation 2 weeks, Production 1 week. Move on when you meet each stage's "ready to move on" checkpoint, not when the time is up.
What should I build to prove I am ready for Generative AI Engineer roles?
A multimodal document question-answering system: Build a system that answers questions over a set of real reports, including their tables, charts and scanned pages. Compare a baseline RAG pipeline with your improved one, and decide with evidence whether fine-tuning is worth it. Deliver 5 things, including a repository and README that runs ingestion and the question-answering API and an evaluation set of at least 50 questions, a third of them about tables or charts.
Is the Generative AI Engineer roadmap free?
Yes. Every roadmap, series and progress tracker on Learn AI Engineering is free, and progress is saved in your browser without an account.