教程中心

Claude API vs OpenAI API 开发者对比（2026）：Claude 强在 Agent 编码/1M 上下文标准价/指令遵循，OpenAI 强在多模态广度/生态体量。含模型阵容与官方定价、API 设计差异（思考控制/采样参数/缓存哲学）、生产级答案：网关路由两家都用。

GPT-4o mini 微调完全指南（2026）：用 OpenAI 微调 API 得到格式/风格稳定的托管模型、海量调用降本。含 JSONL 数据准备→上传→训练→调用真实代码、何时微调 vs 提示/RAG、数据质量 > 数量。

LangGraph enables AI agents with persistent state, conditional branching, and human-in-the-loop workflows. This tutorial builds a real research agent from scratch with memory, tool use, and error recovery.

Beyond basic prompting: master chain-of-thought, self-consistency sampling, tree-of-thoughts, constitutional AI prompting, and systematic evaluation techniques that reliably improve LLM performance.

Deploy open-source LLMs in production with vLLM. Covers GPU selection, Docker setup, Kubernetes orchestration, AWQ quantization for 75% memory reduction, and cost comparison showing break-even vs OpenAI at 5M tokens/month.

Architecture guide for AI startups covering the evolution from prototype to production scale. Includes cost-effective infrastructure choices, avoiding common pitfalls, and when to invest in custom ML.

Fine-tuning LLMs allows adapting powerful foundation models to specific domains without training from scratch. This guide covers LoRA and QLoRA for parameter-efficient fine-tuning, dataset preparation and quality filtering, instruction tuning format, RLHF and DPO for alignment, fine-tuning on consumer GPUs with quantization, evaluation with domain benchmarks, and deploying fine-tuned models with vLLM or TGI for production serving.

Most RAG tutorials only show the happy path. This guide builds a production-ready RAG system covering chunking strategies, embedding selection, reranking, evaluation, and edge case handling.

Retrieval-Augmented Generation (RAG) is the dominant pattern for grounding LLMs with up-to-date knowledge. This guide covers vector database selection (Pinecone, Weaviate, Qdrant, pgvector), embedding model selection and optimization, chunking strategies for documents, hybrid search (vector + keyword), re-ranking, evaluating RAG quality, and deploying production RAG systems that stay accurate over time.

AI agents go beyond chatbots—they use tools, maintain memory, plan multi-step tasks, and collaborate with other agents. This guide compares LangChain, LangGraph, AutoGen, and CrewAI for different use cases, covers reliable agent design patterns, tool calling best practices, memory architectures (short-term, long-term, episodic), handling errors and hallucinations, and deploying production agents with observability.

LLM 推理优化：vLLM、TensorRT-LLM 与规模化服务（2026）：KV 缓存是瓶颈——PagedAttention + 连续批处理是最大吞吐杠杆。vLLM vs TensorRT-LLM 选型、量化/投机解码/前缀缓存/选小模型等其余手段。

Comprehensive comparison of CrewAI, AutoGen, and LangGraph for multi-agent AI systems. Covers role-based collaboration, conversation agents, state machines, and production deployment patterns.

Complete 2026 comparison of Pinecone, Qdrant, pgvector, and Weaviate. Includes Python code examples, performance benchmarks at 1M vectors, filtering, and self-hosting setup.

Complete developer comparison of OpenAI API, Anthropic API, and Google Gemini API for 2026. Covers authentication, streaming, function calling, structured output, rate limits, and cost comparison.

Complete TypeScript guide for AI application development using Vercel AI SDK. Covers streaming chat, tool calling, structured generation, multi-model routing, and production deployment.

Comprehensive guide to deciding between fine-tuning and RAG for LLM applications. Covers fine-tuning GPT-4o mini, LoRA training with Hugging Face, cost comparison, and use case decision framework.

Essential system design patterns for production AI applications: token budgeting, response caching, fallback chains, circuit breakers, and monitoring. Reduce costs 60-80% while improving reliability.

Complete guide to building production AI applications with FastAPI, LangChain, and Supabase in 2026. Covers project setup, async AI endpoints, RAG pipeline, vector search, and deployment.

Comprehensive guide to testing AI applications including unit testing LLM calls, evaluation frameworks like RAGAS and DeepEval, regression testing, and continuous evaluation in CI/CD.

Learn to implement real-time AI response streaming using Server-Sent Events and WebSockets. Build ChatGPT-like streaming UIs with Next.js and FastAPI.

Design and implement real-time personalization using AI, covering user profiling, content ranking, A/B testing, and multi-armed bandit algorithms for continuous optimization.

Build intelligent code review tools using LLMs that go beyond traditional linters. Detect security vulnerabilities, suggest refactoring, explain complex code, and enforce team conventions automatically.

Complete Gemini API tutorial with multimodal inputs, function calling, Google Search grounding. Gemini Flash is 15-20x cheaper than GPT-4o for equivalent quality on many tasks. Includes setup and code examples.

Learn to implement comprehensive observability for LLM applications using LangSmith, Langfuse, and Helicone. Monitor latency, costs, errors, and output quality in real-time.

Prompt engineering has evolved from simple instructions to sophisticated techniques that dramatically improve LLM reliability and output quality. This guide covers chain-of-thought prompting, few-shot examples, self-consistency, ReAct (Reasoning + Acting), structured output extraction with Instructor and Pydantic, system prompt design, and building a prompt testing and versioning discipline.

Multimodal AI combines vision and language understanding to unlock powerful real-world applications. This guide covers GPT-4V, Gemini 1.5 Pro, Claude 3 Opus vision capabilities, open-source models (LLaVA, Qwen-VL), document intelligence with OCR + LLM, building visual QA systems, video understanding, and deploying multimodal AI applications in production.

Serving LLMs in production requires careful optimization to achieve cost-effective performance at scale. This guide covers continuous batching with vLLM, NVIDIA TensorRT-LLM for GPU-optimized inference, speculative decoding, flash attention, KV cache optimization, INT4/INT8 quantization with AWQ and GPTQ, and benchmarking LLM serving systems to find the right performance/cost tradeoff.

Learn proven strategies to dramatically reduce AI inference costs including model selection, caching, batching, prompt optimization, and intelligent routing.

Learn to build semantic search systems using embeddings, vector databases, and re-ranking. Covers hybrid search combining BM25 with dense retrieval for production search applications.

Build a powerful AI-powered Slack bot for DevOps automation including deployment commands, incident management, on-call queries, and intelligent runbook execution via natural language.

Implement AIOps practices including ML-powered anomaly detection, automated root cause analysis, predictive capacity planning, and self-healing infrastructure for modern cloud environments.

Modernize QA automation with AI including LLM-generated test cases, visual regression testing with AI comparison, self-healing test selectors, and natural language test specification.

Learn to build Model Context Protocol (MCP) servers to connect Claude and other LLMs to databases, APIs, and custom tools, enabling powerful AI-native integrations for enterprise applications.

Comprehensive guide to practical techniques for reducing LLM hallucinations in production systems, including RAG, retrieval verification, self-consistency sampling, and chain-of-verification prompting.

Build an intelligent search system combining Elasticsearch with AI for semantic understanding, neural reranking, personalized autocomplete, and query expansion for superior search relevance.

Build production computer vision systems using YOLO v11 for object detection, including custom training, model optimization with TensorRT, edge deployment, and real-time video stream processing.

Build production Named Entity Recognition systems for custom entity types using spaCy and transformer models, covering annotation strategies, active learning, and deployment optimization.

Build production sentiment analysis systems comparing traditional fine-tuned BERT approaches with modern LLM-based classification, including multi-aspect sentiment, emotion detection, and real-time analysis.

Build a production document Q&A system from PDF parsing and chunking through vector indexing, RAG-based answering, citation extraction, and enterprise deployment with access controls.

Build production anomaly detection systems for time series data using statistical methods, isolation forest, LSTM autoencoders, and modern time series foundation models for infrastructure and IoT monitoring.

Complete guide to building a production RAG application using LlamaIndex for orchestration, Qdrant for vector storage, and comprehensive evaluation with LlamaIndex evaluation modules.

Design and implement AI-powered translation systems for global products using neural machine translation, LLM-based localization, quality estimation, and efficient human post-editing workflows.

Comprehensive guide to LLM application architecture patterns from simple prompt-response to complex multi-agent systems, with a decision framework for choosing the right architecture.

Master reliable structured output extraction from LLMs using JSON Schema mode, function calling, Pydantic validators, and instructor library for zero-failure parsing in production.

Design production-grade AI content moderation systems for text, images, and video, covering classification models, human review workflows, policy management, and appeals processes.

Build a complete AI application using PostgreSQL with pgvector extension for vector storage, Supabase for backend, and Next.js for frontend, implementing semantic search and RAG functionality.

Design patterns and best practices for building robust AI-powered REST and WebSocket APIs including streaming responses, idempotency, rate limiting, versioning, and managing non-deterministic outputs.

Build enterprise AI applications with Microsoft Semantic Kernel including plugin architecture, AI planners, memory management, and integration with Azure OpenAI for production-grade orchestration.