Episode Details

When you ask an AI about something new, where does it find the answer? Host A and Host B explore the fundamental difference between RAG (Retrieval-Augmented Generation) and context skills—two competing philosophies for teaching AI agents knowledge beyond their training data. Using a professional kitchen's mise en place discipline as their guide, they break down how knowledge layers work, why trust matters, and why the future belongs to layered approaches, not single solutions. 00:00 - The core problem: Knowledge cutoffs and frozen training data 03:20 - Mise en place: The kitchen metaphor explained 05:50 - RAG: Real-time retrieval and dynamic lookup 10:15 - Context skills: Pre-loading and structured knowledge 13:45 - Layering strategies: When to use each approach 15:30 - 2026 frontier: Hybrid architectures and knowledge trust --- Sources & further reading: • Patrick Lewis et al. — "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (2020): https://arxiv.org/abs/2005.11401 • VentureBeat — "The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next": https://venturebeat.com/data/the-rag-era-is-ending-for-agentic-ai-a-new-compilation-stage-knowledge-layer-is-what-comes-next • arXiv — "Skill Retrieval Augmentation for Agentic AI": https://arxiv.org/html/2604.24594v1 • Andrej Karpathy — context engineering framing (June 2025, expanded 2026) — (originally Twitter/X posts) • The Context Graph — "Context Engineering in 2026: From Karpathy's Tweet to Production Infrastructure": https://thecontextgraph.co/memos/context-engineering-2026-from-tweet-to-infrastructure • AI Builder Club — "Andrej Karpathy's AI Engineering Playbook (2026)": https://www.aibuilderclub.com/blog/karpathy-ai-engineering-playbook • Towards Data Science — "Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI": https://towardsdatascience.com/beyond-rag/ • RAGFlow — "From RAG to Context — A 2025 year-end review of RAG": https://ragflow.io/blog/rag-review-2025-from-rag-to-context • Redis — "Context Retrieval for AI Agents: What It Is & Why It Matters": https://redis.io/blog/context-retrieval-for-ai-agents/ • Neo4j — "Knowledge graph vs. vector RAG: Benchmarking, optimization levers": https://neo4j.com/blog/developer/knowledge-graph-vs-vector-rag/ • CMARIX — "RAG & AI Trust Statistics 2026: Beating Hallucinations": https://www.cmarix.com/blog/rag-ai-statistics/ • yAI — "Why Citation-Based RAG Still Hallucinates": https://yaihq.com/research/citation-based-rag-still-hallucinates • arXiv — "FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAG": https://arxiv.org/pdf/2601.05866 • arXiv — "Knowledge Activation: AI Skills as the Institutional Knowledge Primitive": https://arxiv.org/pdf/2603.14805 • Startup GTM — "Build Karpathy's Self-Updating AI Knowledge Base in Just 90 Minutes": https://startupgtm.substack.com/p/self-updating-ai-wiki-knowledge-base • Atlan — "Knowledge Graph vs RAG: When Each One Wins (2026)": https://atlan.com/know/knowledge-graphs-vs-rag-for-ai/ This podcast episode was fully generated by AI — research, script, voices, and production. Built with Claude, Piper TTS, and automated pipeline tooling.