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CodeNodeX AI 社区管理员，关注 AI 编程、Next.js 和独立开发。
Complex tasks often need multiple LLM calls, but deciding how to split the chain, pass intermediate results, and recover from errors is an engineering problem. This article covers sequential, parallel, routing, and loop patterns; when to use chains versus Agents; state management; and cost-quality tradeoffs.
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.
RAG is not a "single response": retrieval and generation should both stream so users can see progress while they wait. This article covers streaming retrieval, typewriter answer generation, citation rendering, smooth incremental React rendering, and retrieval-to-generation state transitions.
Letting an Agent open a browser, click pages, scrape data, and fill forms—agentic browsing is a high-value AI automation scenario and a deep engineering challenge. Covers architecture (Playwright/Puppeteer + vision models), operation modes (coordinate clicks vs.
AI coding tools reshape more than individual productivity—they reshape team collaboration. This article covers embracing AI: how code review changes, knowledge sharing and best practices, skill evolution (from writing to reading/judging/decomposing), adoption strategy, and norms to avoid black-boxing and over-reliance.
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.
Single-turn conversations are easy; multi-turn conversations are hard. Users follow up, change their minds, refer to prior context, and interrupt. This article covers context management, dialogue state, coreference resolution, topic switching, interruption, and resumption so conversations remain coherent.
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.
Prompt Caching is one of the most effective ways to cut LLM costs, yet many implementations suffer from low hit rates, invalidation, and higher bills. This article explains prefix caching, byte-identical hit requirements, prompt design, TTLs, invalidation, and hit-rate monitoring so caching actually saves money.
Cloud AI is admired for its capabilities but feared for its privacy risks. Local-first principles and on-device AI offer a new approach: data stays on-device, small local models handle routine work, and cloud enhancement is optional. It covers the principles, AI's role, architecture (local CRDT storage, on-device models, and cloud fallback), and use cases.