AI Engineering Roadmap: 16 Topics to Learn, from LLMs to AI Safety

Bashiri Smith · Facebook reel · 2026-08-27 · 0:15 · 99,132 views · Open on Facebook

Topics: Start Here: Roadmaps & Strategy, Retrieval-Augmented Generation (RAG), AI Agents, Tool Use & MCP · Level: beginner

Summary

This is a 15-second reel that reads out a list of AI engineering topics as a study roadmap. The list goes from LLM basics through embeddings, vector databases and RAG, then agents and MCP, then LLMOps, observability, evaluation with LLM-as-a-judge, and AI safety. It ends with machine learning basics and the creator's community. The video doesn't name any specific courses or videos. The caption asks viewers to comment "AI" to get the full roadmap and an invite to the creator's AI engineering community.

Key points

• Topics in the order spoken: LLMs, embeddings, vector databases, RAG, hybrid retrieval, agents, agentic design patterns, multi-agents, agent memory, MCP, LLMOps, observability, LLM as a judge, AI safety and ethics, machine learning basics.
• Retrieval comes before agents: learn embeddings and vector databases, then RAG, then hybrid retrieval (keyword search combined with vector search).
• The agent topics go from single agents to agentic design patterns, multi-agent systems, agent memory, and the Model Context Protocol (MCP) for connecting tools.
• The production topics are LLMOps, observability, and using an LLM as a judge to evaluate outputs.
• AI safety and ethics, and machine learning basics, are listed as separate topics.
• The full roadmap with the actual videos to watch is only available by commenting "AI". The reel itself doesn't list specific videos.

Resources mentioned

• Model Context Protocol (MCP) · docs · modelcontextprotocol.io · free · recommended by both Bashiri Smith & Melvin Vivas
  An open standard for connecting LLM apps and agents to tools and data sources. The official docs explain how it works and how to build servers and clients.
  Also in: Grok Bot Templates: Sharing, Publishing and Safely Installing Bots (Melvin Vivas on X · notes), 8-Week Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), Paste Documentation URLs into Codex Instead of Using MCP (Melvin Vivas on X · notes), OpenAI Agents API: Hosted Codex Harness for Long-Running Cloud Agents (Melvin Vivas on X · notes) and 7 more
• The Complete AI Engineer Roadmap for 2026 (Exact Courses + Step-by-Step) · video · youtube.com · free
  Bashiri Smith's free YouTube training on upgrading your skills and becoming competitive for AI engineering roles, with specific courses listed step by step.
  Also in: SWE-to-AI Engineer Plan for 2027: LLMs, RAG, Agents, Evals, Job Search (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), Software Engineer to AI Engineer Before 2027: A 5-Step Career Plan (Bashiri Smith on Facebook · notes) and 19 more
• BASWE.Ai Engineer (Skool community) · community · skool.com · paid
  The creator's paid community and program, with an AI learning roadmap (including the full ops and evaluation track), daily calls with engineers and recruiters, resume and portfolio help, and a job-search pipeline.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes) and 76 more

Try this

• Comment "AI" on the reel to get the full explained roadmap and the community link.
• Study the topics in order: LLMs → embeddings/vector DBs → RAG/hybrid retrieval → agents/multi-agents/memory/MCP → LLMOps/observability/LLM-as-judge → AI safety, plus ML basics.