#rag
82 episodes · Page 2 of 4
#2026: Prompt Layering: Beyond the Monolithic Prompt
Stop writing giant, monolithic prompts. Learn how to stack modular layers for cleaner, more powerful AI applications.
#2022: When AI Becomes Your IT Department
We dug into a repo of 47 real-world projects showing how OpenClaw powers everything from self-healing servers to overnight app builders.
#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?
#2005: Beyond Vibes: The Hard Science of LLM Evaluation
Running the same LLM on different GPUs can produce different results. Here’s why that happens and how to test for it.
#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.
#1956: AI Skills: From Vibe Coding to Procedural Playbooks
Forget messy system prompts. Agent skills turn AI into a Swiss Army knife of modular, auditable procedures.
#1951: The Digital Ant Farm: Watching AI Agents Build Their Own Society
Explore Moltbook, a social network where AI agents interact with persistent identities and goals, reshaping digital communication.
#1918: When Server Updates Break Your AI Agents
When a third-party MCP server updates its schema, your AI agents can crash. Here's how to build resilient clients that self-heal.
#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...
#1817: The Hidden Taxonomy of AI: Why Specialized Models Outperform Giants
Explore the vast ecosystem of niche AI models for computer vision and document understanding, far beyond large language models.
#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.
#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.
#1778: Audio Is the New "Read Later" Graveyard
Why listening to AI conversations beats reading dense PDFs, and how serverless GPUs make it cheap.
#1765: The Agentic Internet: A Clean Web for Machines
We explore the tools building a parallel, machine-readable web—from SearXNG to Tavily.
#1764: Your Repo as a Knowledge Base
How to give AI agents instant memory of your entire project—without cloud costs or complex infrastructure.
#1754: From Ollama to Agentic CLIs: The Rise of the AI Harness
Explore the evolution from local LLMs to modern agentic CLIs, focusing on the "harness" that gives models context, tools, and autonomy.
#1737: Nous Research: The Decentralized AI Lab Beating Giants
Meet Nous Research, the decentralized collective outperforming billion-dollar labs with open-source AI and the self-improving Hermes-Agent framework.