7 AI Engineering Concept Pairs: RAG vs Fine-Tuning, Agents vs Workflows & More

Bashiri Smith · Facebook reel · 2026-08-27 · 1:47 · 68,298 views · Open on Facebook

Topics: Retrieval-Augmented Generation (RAG), AI Agents, Tool Use & MCP, LLMOps, Deployment & Monitoring · Level: beginner

Summary

A quick comparison of seven pairs of AI engineering concepts that people often mix up. It covers RAG vs fine-tuning, vector databases vs knowledge graphs, agents vs workflows, context vs memory, retrieval vs re-ranking, logging vs tracing, and software engineering vs AI engineering. Each pair gets a one-line explanation. The video ends by promoting the creator's AI engineering community.

Key points

• RAG gives the LLM outside information at runtime so it can answer from your data. Fine-tuning changes the model's weights, which changes how the model behaves.
• A vector database retrieves by semantic similarity. A knowledge graph models relationships between entities and lets you traverse those connections.
• An AI workflow follows mostly predetermined steps. An AI agent decides which actions to take, which tools to use and what to do next based on the situation.
• Context is what the model has available for the current request. Memory stores information from earlier interactions and retrieves it later.
• Retrieval finds possibly relevant documents in a larger collection. Re-ranking scores those results again to decide what actually goes into the LLM's context.
• Logging records individual events. Tracing follows a whole request across model calls, tools, retrieval steps and agents so you can see exactly what happened.
• AI engineers still need software engineering basics (code, APIs, databases, infrastructure). They also integrate models, evaluate AI systems and make unpredictable AI components reliable enough for production.
• The creator claims AI engineers are paid at least 20% more than software engineers. This is his claim; the video gives no source.

Resources mentioned

• 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

• Learn the difference in each of the 7 concept pairs well enough to explain it in one sentence.
• Optional: join the creator's AI engineering community through the caption link, or comment "different" on the video to get the link.