Wannabe vs $200K+ AI Engineer: RAG, Agents, Context and Fine-Tuning Mistakes

Bashiri Smith · Facebook reel · 2026-09-27 · 0:51 · 5,631 views · Open on Facebook

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

Summary

A quick comparison of how a beginner and a senior AI engineer approach the same tasks. Basic RAG and LLM-in-a-loop agents are fine for demos but not for real products. Production RAG needs hybrid search, re-ranking, query rewriting and measured retrieval quality. Agents need narrowly scoped tools and deterministic steps, with state managed by LangGraph. Context engineering and evals matter more than prompt tweaking, and fine-tuning comes last.

Key points

• Basic RAG (chunk, embed, store in a vector DB, return the top match) is a demo, not a product.
• Production RAG: hybrid search plus re-ranking, rewriting the query before it reaches the index, and measuring retrieval quality before trusting answers.
• An 'LLM in a loop with tools' left to figure things out burns tokens and ships chaos.
• Better agents: keep tools narrowly scoped, keep deterministic parts deterministic, and manage state with LangGraph.
• Prompts are only about 10% of the job. The real work is making sure the model has the right context.
• Don't start by training models from scratch in PyTorch. Fine-tuning should come last, not first.
• Retrieval, context and evals solve about 90% of problems. Only change model weights when nothing else improves a measured number.

Resources mentioned

• 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
• LangGraph · tool · langchain.com · free
  Open-source library for building stateful LLM workflows as graphs, with branching, loops and human-in-the-loop pauses.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), LangChain vs. LangGraph Explained with One RAG Chatbot (Bashiri Smith on Facebook · notes), 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (Bashiri Smith on Facebook · notes), 8-Week Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes) and 2 more
• PyTorch · tool · pytorch.org · free · recommended by both Bashiri Smith & Melvin Vivas
  Deep-learning framework. Its built-in scaled dot-product attention (SDP) was the baseline in this benchmark.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), AI DevBox v1.3.0: a GPU-ready Docker image with coding-agent CLIs (Melvin Vivas on X · notes), Docker image with coding agents pre-installed on a CUDA + PyTorch base (Melvin Vivas on X · notes), Software Engineer to AI Engineer: Job Boards, Stack, Projects and Learning Sites (Bashiri Smith on Facebook · notes) and 5 more

Try this

• Go beyond basic RAG: add hybrid search, re-ranking and query rewriting.
• Measure retrieval quality before trusting any answer.
• Keep agent tools narrowly scoped, keep deterministic steps deterministic, and manage state with a framework like LangGraph.
• Focus on giving the model the right context rather than over-polishing prompts.
• Solve problems with retrieval, context and evals first. Only fine-tune when nothing else improves a measured number.
• Comment '200k' to get the creator's AI engineer roadmap.
• Upgrade a basic RAG app to production quality with hybrid search, re-ranking, query rewriting and retrieval-quality evals.
• Build a LangGraph agent with narrowly scoped tools and deterministic steps instead of an open-ended LLM loop.