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。

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 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.

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.

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.

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.

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.

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.

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

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.

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

Understand the spectrum of AI agent autonomy levels and how to design appropriate human-AI collaboration patterns for different business contexts and risk tolerances.

Comprehensive comparison of text embedding models on MTEB benchmark including OpenAI text-embedding-3, Cohere Embed v3, BGE, E5, and other open source models for production RAG systems.

Build production document processing pipelines using AI for extracting structured data from PDFs, invoices, contracts, and scanned documents with high accuracy.