AI-Powered Personalized Recruiter Message Generation

Design an end-to-end system that helps recruiters generate personalised outreach: pull the candidate profile, the open role, the recruiter's past outreach style, and produce a draft InMail. Recurring sub-prompts cover tool registration, RAG, LLM fine-tuning vs prompting, draft / human-in-the-loop, and offline + online evaluation. Requirements Functional: Recruiter logs in, browses a list of candidates, picks one, hits "generate message". The system fetches: candidate profile, candidate's recent activity, the open job, the recruiter's past outreach (style + response rate). An LLM-backed pipeline produces a draft message. Recruiter edits and sends. Telemetry feeds back response/no-response signals. Non-functional: Latency budget: under a few seconds end-to-end for the draft. Quality bar: draft must be specific, factually grounded in profile + job, and stylistically aligned with the recruiter. Guardrails: no fabricated experience claims; respect candidate privacy controls. Reported framings as agentic systems: Tool registry — get_candidate_profile, search_past_outreach, pull_job_posting, score_candidate_fit. The model picks tools; the runtime dispatches. RAG layer — retrieve top-K snippets from the candidate's profile / activity and the recruiter's past messages, inject into prompt. Ranker — rerank candidate snippets for relevance to the open role. Drafting — LLM call with instruction template + retrieved context. Human-in-the-loop — explicit edit step before send. Notes Articulate the business goal first: response rate, recruiter time saved, candidate experience. Metric definitions before architecture. Tool calling vs end-to-end fine-tuning is a legitimate trade-off discussion — the interviewer wants to see both options weighed, not a default to "fine-tune everything". Evaluation is graded heavily — offline (response rate proxy, BLEU vs gold message, recruiter-edit distance) plus online (A/B test of response rate, candidate-flag-as-spam rate) plus guardrail eval (hallucinated-fact rate from an LLM judge). Calibration matters when downstream auctions / quotas use the score — discuss isotonic / Platt scaling if asked. For the leveling-attribute variant (the candidate → category + attribute classification), the same pipeline reused for inference but with retrieval over a skills taxonomy. Preparation Build a mental seven-step framework for any LLM system: business goal → metrics → data → retrieval → model → guardrails → eval. Use it as the round's structural backbone. Practice the tool-calling sequence diagram — registry, model selection, dispatch, result-into-context. Drill the offline-vs-online evaluation split; interviewers explicitly grade whether the candidate proposes both. Be ready with three guardrails (hallucination detector, PII filter, recruiter-style adherence) — generic safety hand-waves do not earn signal.