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Read More →Stage-by-stage Generative AI roadmap PDFs from Uncodemy — from Python and ML basics to LLM apps, RAG and evaluation.
All three PDFs are free to download — no form, no sign-up.
Three PDFs, each covering a different part of the roadmap. Click the sticky download button to get started.
The full 8-stage roadmap in one PDF: Python foundations, ML basics, how LLMs work, prompting, building with APIs, retrieval and RAG, evaluation and safety, and deployment. For beginners and career switchers planning a structured path into Generative AI.
Stages 1–4 in depth: Python, APIs and JSON, basic statistics, supervised vs unsupervised learning, how transformers work at a concept level, and reliable prompting techniques. For learners building their first solid foundation.
Stages 5–8 in depth: building with APIs, embeddings and vector search, RAG architecture, test sets and hallucination checks, privacy and prompt injection, and cost, latency and monitoring for production.
Work through the stages in order — each one builds on the last. This is the same sequence used in every Uncodemy Generative AI course.
| Stage | Learn | Outcome |
|---|---|---|
| 1 Foundations | Python, APIs and JSON, basic statistics | You can write scripts and call a web API |
| 2 ML basics | Supervised vs unsupervised learning, evaluation | You understand how models learn and how they are judged |
| 3 How LLMs work | Tokens, context window, transformers at concept level | You can explain what an LLM is and its limits |
| 4 Prompting | Clear instructions, examples, output formats | You get reliable results from a model |
| 5 Building with APIs | Chat calls, parameters, structured output | You can build a simple LLM feature |
| 6 Retrieval and RAG | Embeddings, vector search, chunking | Your app answers from your own documents |
| 7 Evaluation and safety | Test sets, hallucination checks, privacy, prompt injection | You can measure and defend quality |
| 8 Deployment | Cost, latency, monitoring | Your app runs reliably for real users |
Each stage has a clear topic, a set of sub-topics, and a practical outcome so you know when you're ready to move on.
The roadmap is tool-agnostic, but these are the tools and frameworks it references — all covered in Uncodemy's Generative AI course.
This roadmap is designed for anyone who wants a structured path into Generative AI — regardless of whether you're starting fresh or adding GenAI skills to an existing role.
Graduates from any discipline who want a clear, ordered path into Generative AI without getting lost in scattered tutorials.
Start at Stage 1Developers who already code in Python and want to learn prompt engineering, API integration and RAG to build real AI applications.
Start at Stage 3Data scientists adding Generative AI to their toolkit — LLM basics, prompting, embeddings and evaluation workflows.
Start at Stage 3Working professionals moving into AI engineering who need a structured, project-based path with clear outcomes at each stage.
Start at Stage 1ML engineers adding LLM application skills — RAG, prompt optimisation, evaluation and deployment.
Start at Stage 4Product managers who need to understand the technical trade-offs behind LLM features without writing all the code.
Start at Stage 3Learned from thousands of learners going through this roadmap. Read this before you start Stage 1.
Once you've worked through all eight stages and built a portfolio of projects, these are the entry-level and mid-level roles the roadmap prepares you for.
| Role | Core tools | Stages needed |
|---|---|---|
| Generative AI Engineer | Python, OpenAI API, LangChain, RAG | 1–8 |
| LLM Engineer | Python, LangChain, LlamaIndex, OpenAI API | 1–7 |
| RAG Engineer | LangChain, ChromaDB, Pinecone, Python | 1–6 |
| Prompt Engineer | Prompt design, chain-of-thought, API parameters | 1–5 |
| AI Product Manager | Concept knowledge, RAG, evaluation, safety | 1–7 |
| AI Solutions Architect | All of the above + deployment and monitoring | 1–8 |
If you have 10–12 hours per week, this is a simple way to move through the eight stages without getting stuck.
Python, APIs, JSON, basic statistics, supervised vs unsupervised learning and evaluation metrics. Build a script that calls an API.
Tokens, context windows, transformers, zero-shot vs few-shot, chain-of-thought and structured output. Write 10 prompts and compare results.
Chat completions, parameters, streaming and function calling. Build a small assistant that uses an API and one tool.
Embeddings, vector search, chunking and building a RAG pipeline. Build a question-answering app over your own documents.
Test sets, hallucination checks, privacy, prompt injection and guardrails. Run an evaluation on your RAG app and improve it.
Cost tracking, latency, monitoring, FastAPI deployment. Ship your capstone project and document it on GitHub.
More study material and guides that pair with this roadmap.
Prefer guided learning? Explore Uncodemy's Generative AI / Agentic AI course and learn the roadmap stage by stage with a trainer — 100+ live sessions, 160+ learning hours and a 25-hour capstone project.
These PDFs are study material for learning and revision. They are not a certificate, a degree or a promise of a job, and technology changes, so check official documentation for the latest details.
Spot an error? Write to info@uncodemy.com and the trainer team will review it.
Everything you need to know before starting the roadmap.
Start with Python and machine learning basics, learn how large language models work, practise prompting, build applications with APIs and retrieval, then learn evaluation, safety and deployment.
Basic ML knowledge helps you understand training, evaluation and limits, but you can start using LLM APIs with good Python skills and learn ML alongside.
It depends on your background and practice time. Plan the stages first and measure progress by projects you can explain.
Python is the most common language for LLM applications, with JavaScript or TypeScript also widely used for web-based apps.
A question-answering app over a small set of your own documents is a good first project because it teaches prompting, embeddings, retrieval and evaluation together.
No. Most of this roadmap can be completed using APIs (OpenAI, Hugging Face) on a regular laptop. A GPU is only needed if you plan to train or fine-tune models locally, which is optional.
You can build most projects for free or very low cost using free API tiers, open-source models on Hugging Face, and free vector database tiers. The PDFs include a section on managing API costs while learning.
Yes. All three Generative AI roadmap PDFs on this page are completely free to download. No sign-up, payment or form is required — just click the sticky download button.
Grab the Generative AI roadmap PDF and work through the eight stages at your own pace. All three PDFs are free to download — just use the sticky download button.
