Choosing a Knowledge Strategy: RAG vs Graph vs Fine-Tuning vs CAG vs Long Context

Bashiri Smith · Facebook reel · 2026-09-01 · 0:23 · 18,404 views · Open on Facebook

Topics: Retrieval-Augmented Generation (RAG), AI System Design & Architecture, Fine-tuning & Model Customization · Level: intermediate

Summary

This short video covers five ways to give an LLM knowledge or change how it behaves, and matches each one to the problem it fits best: RAG, GraphRAG/knowledge graphs, fine-tuning, cache-augmented generation (CAG) and long context. The creator's point is that knowing which architecture suits a problem is what separates demos from production AI systems.

Key points

• Large external data sources or knowledge bases → use RAG (Retrieval-Augmented Generation): retrieve only the relevant pieces at query time.
• Connected knowledge, where relationships between pieces of data matter → use GraphRAG or a knowledge graph.
• Changing how the model behaves (style, format, task behavior) → use fine-tuning, not retrieval.
• Small, stable knowledge that is reused across many requests → use CAG (Cache-Augmented Generation): preload or cache the context instead of retrieving it each time.
• A one-time, single large dataset or document → use long context: put it straight into the model's context window.
• Rule of thumb: pick the strategy by data size, how connected the data is, how often it changes and how often it's reused, and whether you need new knowledge or new behavior.
• Knowing which architecture fits a problem is what separates demos from production AI systems.

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

Try this

• Memorize the mapping: large external data → RAG; connected data → GraphRAG/knowledge graph; behavior change → fine-tuning; small stable reused data → CAG; one-time large data → long context.
• Watch the free AI Engineer roadmap video to learn these five skills.
• Comment "skills" on the video to get the creator's full training sent to you.
• Optionally, look at the creator's Skool community for career and interview support.