#prompt-engineering
93 episodes · Page 2 of 4
#2558: Should You Say Please to AI?
The surprising cost, technical tradeoffs, and ethical dilemmas of saying "please" to chatbots.
#2540: Does Your AI Framework Change the Output?
Same model, same prompts, different harness. Does the plumbing change the water?
#2534: Can AI Generate Diagrams Without Typo Disasters?
Why AI diagram tools still mangle text labels — and what to do about it today.
#2518: How Jailbreaking Reveals AI's Hidden Tension
What the DAN prompt and grandma exploits reveal about the structural conflict inside every LLM.
#2494: Active Prompt Engineering: Daniel's Diff-Based Loop
A deep dive into iterative prompt refinement using inter-iteration prediction change as an uncertainty signal.
#2471: Creative Briefs for AI Agents: What Agencies Already Know
How agency best practices for briefing creatives map directly onto getting reliable output from AI agents like Claude Design.
#2470: Where Intelligence Should Live in Your Pipeline
When should you fine-tune a tiny model for prompt enhancement instead of prompting a large one? The answer depends on latency, precision, and domain.
#2461: How Claude Code's Conversation Compaction Actually Works
The three-tier system, what survives, what dies, and why you shouldn't rely on auto-compact.
#2413: When Your AI Says No to Everything
Why LLMs refuse 73% of harmless prompts — and the trade-off between safety and usefulness.
#2334: How AI Flattens Your Voice in Emails
Why AI-generated emails feel impersonal and how to reclaim your authentic voice in professional communication.
#2309: Blind Ranking AI's Best Podcast Scripts
How do 15 AI models handle controversial podcast prompts? We rank their scripts blind and reveal the surprising winners.
#2242: AI as Your Ideation Blind Spot Spotter
How to use AI not to answer questions you already know to ask, but to surface possibilities your expertise has made invisible to you.
#2228: Tuning RAG: When Retrieval Helps vs. Hurts
How do you prevent retrieval from suppressing a model's reasoning? We diagnose our own pipeline's four control levers and multi-source fusion strat...
#2219: Spec-Driven Life: How AI Planning Beats Project Paralysis
What makes AI agents reliably productive? A structured spec that externalizes memory and chunks work into manageable pieces. Can the same framework...
#2207: Specs First, Code Second: Inside Agentic AI's New Era
As AI coding agents evolve from autocomplete to autonomous cloud workers, the bottleneck has shifted—now it's about how clearly you specify what ne...
#2192: How We Built a Podcast Pipeline
Hilbert reveals the complete technical architecture behind 2,000+ episodes—from voice memos to GPU-powered TTS, with Claude models, LangGraph workf...
#2191: Making Multi-Agent AI Actually Work
Research from Google DeepMind, Stanford, and Anthropic reveals most multi-agent systems waste tokens and amplify errors. Single agents with better ...
#2175: Let Your AI Argue With Itself
What happens when you let multiple AI personas debate each other instead of asking one model one question? A deep dive into synthetic perspective e...
#2174: Role-Playing as Orchestration
How a role-playing protocol from NeurIPS 2023 became one of AI's most underrated agent frameworks—and what happens when you scale it to a million a...
#2170: Pricing Agentic AI When Nothing's Predictable
How do you charge fixed prices for systems that operate in fundamental uncertainty? Consultants are discovering frameworks that work—but they requi...
#2165: Strip Your Agent to Bash
The frameworks matter less than you think. What separates a working agent from a failing one is the harness—the orchestration, memory, and tool des...
#2133: Engineering Geopolitical Personas: Beyond Caricatures
How to build LLMs that simulate state actors with strategic fidelity, not just surface mimicry.
#2125: Why Agentic Chunking Beats One-Shot Generation
A single prompt can't write a 30-minute script. Here’s the agentic chunking method that fixes coherence.
#2110: Tuning AI Personality: Beyond Sycophancy
AI models swing between obsequious flattery and cold dismissal. Here’s why that happens and how to fix it.