Practical overview
When it fits
Use it to plan a structured course of study or to find a project and decision guide while learning production-oriented GenAI engineering.
Who it is for
- Developers and technical learners who can read Python and understand HTTP.
- Backend, frontend and data engineers, data scientists, ML engineers, and people beginning from zero who want a recommended learning path.
Documented uses
- Choose a learning sequence based on your technical background.
- Study topics spanning LLM calls, RAG, tools, agents, evaluation and production.
- Build portfolio projects with code, tests and evaluation harnesses.
- Compare approaches using guides on topics such as RAG versus long context or API use versus self-hosting.
Requirements & limits
The repository is public and lists an MIT license.
- The roadmap covers 12 stages, from foundations through real-time and voice topics.
- It assumes readers can read Python and understand HTTP.
- The repository describes 15 portfolio projects with source code, tests, evaluation harnesses and deployment documentation.
- LangChain implementations are included, while the roadmap states that its scope is not limited to LangChain.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Choose a learning path and follow the GenAI roadmap
Use the repository’s background-specific learning paths and 12-rung curriculum to structure GenAI engineering study.
Before you start
- Ability to read Python and understand HTTP, as assumed by the roadmap.
Steps
Read LAST_VERIFIED.md to review when factual claims were last checked.
Choose the learning path that matches your background, or the path for starting from zero.
Work through the roadmap rungs in order, following each rung’s link to the next.
Build at least three projects as you progress and run the evals included with each project.
Expected outcome
A structured study path through the repository’s GenAI engineering topics, with project work and evaluation practice.
Guide limitations
- The repository describes itself as a roadmap rather than a tutorial; topic materials assume Python and HTTP familiarity.
- The roadmap is focused on production GenAI engineering and is not exhaustive.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- AdilShamim8/GenAI-Roadmap-with-Notes-and-Projects · Official homepage title · retained in its source language