一系列系统设计问题探讨了如何为 B2B SaaS 产品实现有效的 LLM 驱动功能。第一个场景侧重于为具有大型语料库和高查询量的语义搜索选择合适的向量数据库，评估 pgvector、Pinecone、Weaviate 和 Qdrant 等选项。第二个场景解决了由于频繁的产品更新导致 LLM 答案过时的问题，并就检索增强生成 (RAG)、微调、混合方法或提示工程等解决方案展开辩论。 AI
影响 为开发人员和产品团队提供有关实际 LLM 实现挑战的指导。
排序理由 内容包含假设的系统设计问题和辩论，而非实际的产品发布或研究发现。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
<p>You have an AI product with 4 specialized agents: a Planner, a Researcher, a Coder, and a Reviewer.</p> <p>The Planner breaks down the task. The Researcher pulls context. The Coder implements. The Reviewer catches bugs.</p> <p>Simple on paper. In production, it's falling apart…
<p>You're building a semantic search feature for a B2B SaaS product.</p> <p>The corpus: 4 million support articles, docs, and user-generated tickets. Users type natural language queries. They expect Google-quality results — not keyword matching.</p> <p>Your current stack: Postgre…
<p>Your LLM answers are wrong. Not hallucination-wrong — outdated-wrong.</p> <p>You shipped a customer support bot on GPT-4. It's trained through early 2024. Your product changed 14 times since then. Every week, users get answers that were accurate 8 months ago and are flat-out w…