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CodeNodeX AI 社区管理员，关注 AI 编程、Next.js 和独立开发。
Traditional docs rely on TOCs and search; users who can't find things give up. AI lets docs 'answer directly'—plug in RAG and 'how do I do X?' returns an answer plus citations. This article shows how to upgrade a static docs site to AI Q&A: chunking and indexing, citations, Mintlify/Convex vs self-hosted, and why AI shouldn't replace the docs.
Attack before launch, or users will afterward. LLM red teaming finds prompt injection, jailbreaks, privilege abuse, data exfiltration, harmful output, and denial of service. This article covers adversarial prompt sets, LLM-generated attacks, and turning findings into regression tests so you discover flaws before users do.
Production RAG almost always needs conditional retrieval: search only a department, time window, or product. Vector recall alone cannot filter precisely. This article explains metadata design for chunks, pgvector SQL and Qdrant payload filters, pre-filter performance benefits, and permission isolation.
Cursor and Claude Code are 2026's two most mainstream AI coding tools, but their form factors and philosophies differ—one is IDE-native, the other a terminal agent. This article compares them across form factor, context, agent capability, MCP ecosystem, collaboration style, and pricing, gives a who-uses-which conclusion, and explains why many use both.
You burned $50K in tokens in a month but can't say which feature or user did it — that's no cost attribution. Learn how to attribute AI cost via request tagging, token metering and aggregation, unit economics (per-user/per-feature cost), and anomaly detection, turning AI cost from an opaque ledger into something optimizable and accountable.
Whether a user experiences value in the first minute decides an AI product's retention. This article covers AI onboarding core: shortening TTFV, guiding the first "aha" moment, avoiding information overload, progressive disclosure, lowering the barrier with templates, and driving optimization with activation metrics.
With multiple Agents, the hardest problem is not individual capability but communication: shared state or message passing, protocols, asynchrony, and errors. This article explains blackboards, message passing, handoffs, serialization and contracts, supervisor/pub-sub/peer-to-peer architectures, plus engineering concerns around serialization, versioning, and
When RAG answers poorly, retrieval is usually at fault--but first determine whether the issue is missed recall or irrelevant retrieval. This article presents a systematic workflow for inspecting recall, similarity, reranking, and prompts, plus ways to diagnose bad chunking, embedding mismatches, and overly strict filters.