大模型、多模态、推理模型和 AI 产品趋势
This collection features articles and community discussions in the AI 前沿 space, focusing on real-world engineering scenarios, tool selection, practical paths, and risk boundaries.
The page is continuously updated and serves as a learning roadmap, technology selection reference, and technical review entry point.
Checking the win rate every few dozen items and stopping once significance appears systematically inflates false positives. This article freezes the pairing unit, order randomization, prespecified looks, sequential boundaries, and candidate-family correction into a single auditable comparison.
Using pgvector but finding search slow? This guide explains pgvector performance tuning: choosing IVFFlat vs. HNSW, tuning parameters (lists/m/ef_search), hybrid search (vector + full text), filtering metadata before search, and best practices for million-scale datasets, so pgvector runs quickly and accurately.
After the release system selects a Prompt version, request paths can still mix bundles or continue running a revoked version. This article closes runtime races with session assignments, one-shot permits, and revocation watermarks.
Successfully loading a quantized model does not make it ready for production. This article turns candidate artifacts, calibration data, task quality, tail slices, latency, throughput, and cross-hardware stability into release gates so that average scores cannot conceal localized regressions.
Telling a model not to disclose information during generation is not access control. This article explains identity context, ACL filtering, cache isolation, citation validation, and unauthorized-access testing so the model never sees documents the user should not access.
Updating only the identity store after removing a member does not mean the knowledge is no longer visible. This article uses authorization epochs, decision credentials, invalidation watermarks, and negative probes to bound the RAG revocation propagation window.
RAG citations are not decorative links appended to an answer. This article covers claim-to-chunk mapping, citation granularity, evidence coverage validation, invalidation handling, and interface design so verifiability becomes a system capability.
RAG costs extend beyond model tokens. This article presents a strategy for allocating budgets by query value, retrieval path, reranking, context size, and cache hits, with rollback-ready quality guardrails.
A vector database in a designated region does not mean RAG data never crosses borders. This article uses versioned residency policies, regional evidence, and egress gates to cover parsing, model calls, caches, logs, and recovery.
A successful indexing job does not mean users receive the latest knowledge. This article covers freshness SLOs, source change detection, priority queues, stale-answer handling, and operational reporting to make RAG accountable for content currency.
Switching embedding models is not as simple as rebuilding an index and routing traffic to it. This article covers versioned vectors, dual writes and reads, offline relevance evaluation, phased cutovers, and rollback criteria to reduce sudden retrieval-quality regressions.
Stable offline RAG metrics do not guarantee a stable production experience. Learn how to maintain gold sets, detect query-distribution drift, connect production feedback, and tune retrieval and generation through controlled experiments.