In this blog, we're gonna walk through a fictional story, while learning how to optimize LLMs for cost, and the associated tradeoffs. Tuesday, 10 June, 2:14 PM PST The billing alert hit. I was halfway through a product demo, nodding along to myself on Zoom, saying something

If your AI agent is returning the wrong context, it’s probably not your LLM, but your embedding model. Embeddings are the hidden engine behind retrieval-augmented generation (RAG) and memory systems. The better they are, the more relevant your results, and the smarter your app feels. But here’s the

You’ve already met our guide on implementing short-term conversational memory using LangChain, which is great for managing context inside a single chat window. But life, therapy, and enterprise apps sprawl across days, weeks, and years. If our agents are doomed to goldfish-brain amnesia, users end up re-explaining everything from

Chatbots that don’t remember conversations are very frustrating to work with. Users treat AI like a human and expect it to remember. LangChain recently migrated to LangGraph, a new stateful framework for building multi-step, memory-aware LLM apps. So while the docs might still say “LangChain memory,” what you’re