Overview
LLMs are stateless. Every conversation starts from zero, and every fact your model does not have is either a hallucination or a retrieval problem you have not solved yet. This is the day you solve it properly. You will chunk a real corpus four different ways and score each strategy against the same question set, build a FAISS index and choose its type on measured latency and recall rather than on a blog post, then give an agent a memory hierarchy and prove it works the only way that counts — clear the notes, run the same task again, and watch it repeat a step it used to skip. This was Day 2 of the two-day Context Engineering intensive and now stands on its own. You do not need Day 1. If you already have the foundations, start here.
Where This Class Leads
What you leave able to do
- design a packaged assistant over your documents
- design a recursive summariser for oversized documents
- re architect an agent as a stateless reducer
- reconcile an agents run with a tool that failed
- select among agent architectures
- synthesise a memory hierarchy for an agent
- synthesise an agent that carries notes across its own runs
How you show it. Paired runs of the same multi-step task with the note store kept and cleared, where the cleared run repeats a step the noted run skips, plus the note text the agent wrote and the later turn that cites it.
It sits on one path
🤖 Build production AI systems from prompt to deployment. 8 of 13 after Prompt Engineering · before Advanced Retrieval Augmented GenerationAbout 25 hours of practice sit behind this one. See what your hours buy.
Join Us
- Your seat in the class
- Lifetime access to materials
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Contact us: liz@themultiverse.school