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.
Vector retrieval (bi-encoder) is fast but coarse; cross-encoders are accurate but too slow for recall. ColBERT's late-interaction is a third path — precision near a cross-encoder, speed near a bi-encoder. This article explains the three models' trade-offs, the late-interaction principle, when to use it, and implementation with RAGatouille/ColBERTv2.
Voice AI has moved from toy demos to production-ready systems for real-time transcription, voice conversations, dubbing, and translation. This guide covers the ASR/TTS/assistant stack, product scenarios, streaming ASR, low-latency TTS, VAD, interruption handling, multimodal architectures, and practical pitfalls.
AI applications have more failure points than traditional software: model APIs, vector databases, tools, and internal services can all fail. This article covers degradation, circuit breaking, retries, fallbacks, multi-model and multi-provider recovery, backups for vector stores, memory, and sessions, monitoring, and chaos drills to keep AI apps resilient.
The real direction for RAG optimization lies not in papers but in users' failure cases: wrong answers, missed evidence, and irrelevant responses. This article presents a failure-case-driven loop for collecting signals, classifying root causes, applying targeted fixes, and adding cases to an eval set to prevent regressions.
"Querying a database in natural language" sounds appealing, but production introduces difficult problems in schema understanding, ambiguity, SQL correctness, and injection security. This article presents a complete production-grade Text-to-SQL architecture for safe natural-language data access.
Every AI application must choose between GPT/Claude and Llama/Qwen. This article explains the tradeoffs in capability, convenience, data ownership, control, cost, and customization; how privacy, scale, and compliance shape the choice; and why a hybrid of closed-source primary models and open-source fallbacks often works best.
Tokens are the billing unit and context currency of LLMs, yet many developers understand them only vaguely. How many tokens does a Chinese character use? How much does code consume? Why does tokenization affect cost and quality?
Your AI product must block illegal and abusive content without killing legitimate content. This article walks through moderation system design: dimensions (input/output/UGC), multi-layer defense (rules/classifiers/LLM review), false-positive balance, appeals and human review, and OpenAI Moderation vs. self-built classifiers.
Fine-tuning follows one rule: garbage in, garbage out. This guide covers instruction-pair formats, data collection and cleaning, quality controls such as deduplication, denoising, and consistency checks, synthetic-data uses and risks, augmentation, and the right way to use LLMs to generate training data.
Whether search works comes down to relevance: do results match what the user searched for? Covers signals (semantic, keyword, authority, freshness, personalization), weighted fusion, offline evaluation (labels + metrics), click feedback, and avoiding results that look relevant but are useless.
Reasoning models such as o1, o3, and DeepSeek-R1 "think before answering." They excel at complex math, code, and multistep reasoning but are slower, costlier, and harder to control. This article explains where they help or hurt and presents a hybrid architecture that reserves them for genuinely hard reasoning.
Changing embedding models, revising chunking, and rebuilding indexes can cause downtime or degrade retrieval in production RAG. This article covers blue-green indexes, dual writes, shadow traffic, rebuild-and-switch workflows, and incremental updates that make these changes invisible to users.