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.
There are three ways to improve a model in a domain: Prompting, RAG, and fine-tuning. Most teams choose incorrectly, fine-tuning problems suited to RAG or adding RAG where a prompt would suffice. This article offers a decision framework for knowledge, behavior, and style requirements, including when to combine approaches.
When a RAG system retrieves contradictory content from different versions, permission scopes, or sources, it cannot let the model choose the most convenient answer. This article covers source ranking, conflict detection, temporal semantics, and a safe clarification workflow.
A document entering a vector database does not mean the knowledge base is correct. Learn versioning, event deduplication, deletion tombstones, retries, and consistency checks for incremental indexing so RAG does not keep citing stale or deleted content.
When RAG slows down, teams often focus only on the model's first token. This article uses latency budgets to break down retrieval, filtering, reranking, generation, and network overhead, with production strategies for risk-aware adaptive degradation.
Changing a `role` field to a policy ID is not an ordinary mapping change. This article uses a versioned ACL schema, dual-write backfills, a coverage ledger, and negative-query proof to ensure there are no authorization gaps during migration.
Not every question should go to the same vector retriever. This article designs an observable query routing policy that selects keyword, semantic, structured queries, or safe refusal by intent, data freshness, permissions, and confidence.
Indexes with the same name can come from different corpora, parsers, or permission coverage. Treat RAG indexes as immutable artifacts, using signed manifests, digest verification, and atomic aliases for promotion and rollback.
Being fetched by a connector does not mean a document belongs in the production knowledge base. This article uses source registration, quarantined parsing, risk gates, and release binding to control the admission and revocation of untrusted documents.
"Enterprise knowledge base Q&A is among the most valuable RAG use cases, yet its difficulty is often underestimated. The challenge is not retrieval, but the full pipeline: data ingestion, permissions, updates, and evaluation. This guide covers multi-source ingestion, permission isolation, incremental updates, evaluation, and enterprise-specific pitfalls."
VAD alone cannot produce reliable speech turns. This article uses monotonic audio sequences, an endpointing state machine, pre-roll and trailing silence, interruption cancellation, and bounded backpressure to control real-time voice pipelines.
Changing a tokenizer affects token IDs, length budgets, special tokens, and cache semantics. This article presents a verifiable migration process based on artifact fingerprints, golden corpora, compatibility levels, dual-track routing, and versioned data.
As context windows grow from 4K to millions of tokens, many ask whether everything can simply be placed in context and RAG discarded. This article compares cost, retrieval, updates, and traceability to show where each excels, why they work best together, and why RAG remains the better choice for most production workloads.