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How do you know if your AI product is quietly giving users wrong answers? Learn how LLM observability works: traces, spans, LLM-as-judge, and why a 200 OK status code tells you nothing about quality. (Remember to click on"show pictures")

LLMs also think and reason. They generate intermediate steps, check its own logic, and build toward an answer piece by piece.

In two years, Temu became the most downloaded app in America, made its founder the richest person in China, and helped kill Forever 21. Then a single policy change broke the whole thing.

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.