教程中心

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.

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.

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.

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.

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.

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.

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.

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

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

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 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.

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.

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

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.

Master AI function calling and tool use patterns for building reliable agents, covering tool design, error handling, parallel tool execution, and preventing tool abuse.

Practical strategies to dramatically reduce LLM API costs including semantic caching, intelligent model routing, prompt compression, request batching, and monitoring cost per feature.

Production guide for LangChain applications covering caching strategies, error handling, observability with LangSmith, cost optimization, and common anti-patterns to avoid.

Learn to generate high-quality synthetic data for AI training using LLMs, GANs, and diffusion models. Covers data augmentation, privacy-preserving synthesis, and evaluating synthetic data quality.

Technical guide to deploying AI models on edge devices including mobile phones, IoT devices, and edge servers using Apple CoreML, Android NNAPI, MLC-LLM, and hardware-specific optimizations.

Go beyond basic RAG implementation to build production-grade retrieval-augmented generation systems with query rewriting, reranking, corrective mechanisms, and comprehensive evaluation.

Design and implement enterprise semantic search systems that combine vector embeddings, BM25 keyword search, and LLM reranking for accurate, fast, and contextually relevant results.