personalization

Cross-session memory is what makes an assistant feel personal, and it is mostly a retrieval and state-management problem rather than a bigger context window. The signal is the tiered architecture plus what to store, forget, and protect.

Summarize, draft, and triage, all across the most sensitive PII a person holds. The signal is grounding in the real thread, matching the user's voice, and a firm human-in-the-loop rule on anything that gets sent.

E-commerce search is where retrieval, ranking, and business goals meet, and it carries a strict exact-match requirement (brand, size, SKU). The signal is query understanding plus hybrid retrieval plus business-aware ranking. Here is the design.

Notification systems fail by spamming users until they mute, and a mute is a permanently lost channel. The signal is treating it as a per-user send/hold/batch/suppress decision optimized for long-term trust, not for clicks or volume.

Personalizing an LLM is a retrieval and memory problem, not a per-user fine-tune. Learn how to assemble user context at request time, keep long-term memory from bloating the prompt, and respect privacy and the right to be forgotten.

The LLM is the easy part. The system is a learner model: a skill graph, a per-student mastery estimate, and an item selector that keeps the student at the edge of their ability. Here is the design, including the pedagogical constraint most candidates miss.