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Build scalable personalization systems for LLM applications using user profiles, embedding-based preferences, and privacy-preserving context injection techniques.
Implement hybrid search combining keyword BM25 with semantic embeddings, ranking, and LLM-powered query understanding.
Implement per-user token budgets, tiered model access, request queuing, cost attribution, real-time dashboards, and anomaly detection to prevent AI bill shock.
Build recommendation systems using embeddings, two-tower models, and solve cold start with hybrid approaches.
Comprehensive guide to red teaming LLMs including jailbreak testing, prompt injection, bias testing, adversarial robustness, and privacy attacks.