Blog

Most AI teams default to frontier models and overpay 5x. Learn how model routing, fine-tuning, and right-sizing cut costs by 60-70% without hurting quality.

Anthropic walked away from a $200M Pentagon contract, got banned from government use, and watched Claude hit #1 on the App Store. All in the same week. Here's what happened.

Perplexity AI is challenging Google Search with direct answers, fast shipping, telecom deals, a $34.5B Chrome bid, and its own AI browser, Comet.

Real AI chatbots failed at Chevrolet, DPD & Air Canada. Here’s how guardrails prevent hallucinations, PII leaks, prompt injection & costly AI mistakes.

Uber Eats search couldn't handle 'gf pizza' or 'something spicy.' Here's how they rebuilt it with semantic embeddings and kept it running at scale.

Peloton went from a $307K Kickstarter to $50 billion — then lost 97% of its value. A product case study on the decisions behind tech's biggest collapse.

Here's how DoorDash replaced 300 fixed carousels with LLM-generated ones, used embeddings, AI moderation, ranking, and A/B tests to boost relevance and clicks.

Search, recommendations, chatbots that answer questions all start the same way: converting something into an embedding. As PMs it is essential to understand how embeddings work and how to use them.

If you want to make an AI product work as intended, you need to know how to use data, feedback loops, memory, and guardrails

LLMs understand the world, but are they actually useful? This guide helps you understand how to build a reliable AI system—covering the exact frameworks PMs need to master Prompts, Agents, and Quality

Learn how RAG and embeddings make AI reliable and smart. RAG helps AI find truth, while embeddings help it understand meaning and user intent. Details inside.