RAG
166 episodes · Page 5 of 7
#2133: Engineering Geopolitical Personas: Beyond Caricatures
How to build LLMs that simulate state actors with strategic fidelity, not just surface mimicry.
#2129: Shifting Left on Hallucinations
Stop hoping your AI doesn't lie. We explore the shift to deterministic guardrails, specialized judge models, and the tools making agents reliable.
#2113: Goldfish vs Elephant: The Stateful Agent Dilemma
Stateless agents are cheap and fast, but stateful ones remember your window seat. Which architecture wins?
#2066: The Transformer Trinity: Why Three Architectures Rule AI
Why did decoder-only models like GPT dominate AI, while encoders and encoder-decoders still hold critical niches?
#2057: How Agents Break Through the LLM Output Ceiling
The output window is the new bottleneck: why massive context doesn't solve long-form generation.
#2011: Saving AI Knowledge Beyond the Chat Window
We're brilliant at prompting AI, but terrible at saving the answers. Here's why that "digital masterpiece on a chalkboard" vanishes.
#2010: Building Better AI Memory Systems
We obsess over AI inputs but treat outputs like Snapchat messages. Here's why that's a massive blind spot.
#2008: Needle-in-a-Haystack Testing for LLMs
New AI models claim to be genius-level, but can they actually find a specific fact in a massive document?
#1994: Why Can't AI Admit When It's Guessing?
Enterprise AI now auto-filters low-confidence claims, but do these self-reported scores actually mean anything?
#1959: How Constrained AI Models Handle the Unexpected
Your AI assistant promised to only use your documents. Instead, it invented a case law that doesn't exist. Here's why.
#1931: Where Your AI Pipeline Actually Dies
Why do AI pipelines crash? It’s not the models—it’s the plumbing. We break down how to manage data between stages.
#1929: From Vibe Checks to Model Metrics
We stopped "vibe-checking" our AI scripts and built a science fair for models. Here's how we grade them.
#1914: Google Invented RAG's Secret Sauce
Before LLMs, Google solved the "hallucination" problem with a two-stage trick that's making a huge comeback.
#1907: Why We Still Fine-Tune in 2026
Despite million-token context windows, fine-tuning remains essential. Here’s why behavior, not just facts, matters.
#1838: Tuning Search Without Losing Your Mind
Modern search bars are AI decision engines. Here's how small teams can tune fuzzy matching, semantic search, and reranking without breaking everyth...
#1834: Owning Your AI Memory: The Data Exit Strategy
Why your AI remembers your coffee order but forgets your son’s name—and how to build a portable, federated memory layer you actually own.
#1828: Mastering 2M Token Context in Agentic Pipelines
A massive context window sounds like a dream, but it can quickly become a nightmare for complex AI workflows.
#1812: When AI Gets a Truth Tether to the Talmud
Sefaria's new MCP server connects AI directly to 2,700 years of Jewish texts, transforming how scholars and curious learners study ancient literature.
#1804: The Fork in the Road: Why AI Agents Check Old Receipts First
Stop your AI agent from overthinking. Learn why it checks old memories instead of booking flights—and how to fix the "eagerness" problem.
#1799: The Original AI Blueprints: BERT & CLIP
Before GPT, two models changed everything. Discover how BERT and CLIP taught machines to read and see the world.
#1794: RAG Is Cheaper Than You Think (Until It’s Not)
From a $1 embedding bill to a $10k/month vector database bill, here’s the real math behind RAG in 2026.
#1792: Google's Native Multimodal Embedding Kills the Fusion Layer
Google’s new embedding model maps text, images, audio, and video into a single vector space—cutting latency by 70%.
#1784: Context1: The Retrieval Coprocessor
Chroma's new 20B model acts as a specialized "scout" for your LLM, replacing slow, static RAG with multi-step, agentic search.
#1779: AI Memory Is a Mess: Files, Vectors, or Cloud?
Why your AI forgets your instructions and what the battle over portable memory means for the future of agents.