教程中心

Fine-tuning is often unnecessary—but when it's the right choice, it delivers significant improvements. This guide covers: when fine-tuning beats prompt engineering (with decision framework), LoRA and QLoRA parameter-efficient fine-tuning explained, preparing training data (quality over quantity), evaluating fine-tuned models, deploying fine-tuned models in production, and cost analysis across fine-tuning providers (OpenAI, Together AI, Fireworks AI, self-hosted). Includes hands-on examples with real training code.

AI evaluation is the difference between AI that works in demos and AI that works in production. This guide covers building comprehensive eval suites: metric design for different task types, automated vs. LLM-based evaluation, human evaluation methodology, regression testing for model updates, A/B testing AI systems, and evaluation infrastructure using open source tools (RAGAS, HELM, DeepEval) and cloud platforms.

AI agents—autonomous systems that use tools and make decisions to complete multi-step tasks—are moving into production at enterprise scale. This guide covers reliable agent architecture: tool design and error handling, state management for long-running agents, human-in-the-loop patterns, observability and debugging agents, graceful failure modes, security considerations, and testing strategies for non-deterministic systems.

LLM API costs can spiral quickly: a production application making 1M requests/day at $0.01 average = $3,000/month. This guide covers comprehensive cost optimization strategies: prompt compression, intelligent model routing (use GPT-4 only when needed), caching strategies, batch processing optimization, output length control, model selection framework, and architecture patterns that dramatically reduce per-request cost without meaningful quality degradation.

Multimodal AI—systems that understand and generate text, images, audio, and video together—enables a new category of AI applications. This guide covers multimodal model architectures (GPT-4V, Gemini Pro Vision, Claude 3 Vision), building vision-language applications, document intelligence with layout understanding, audio-language models for transcription and analysis, video understanding with temporal reasoning, and production deployment considerations for multimodal systems.

Vector databases power modern AI applications: semantic search, RAG pipelines, recommendation systems, anomaly detection. This deep dive covers vector similarity search algorithms (HNSW, IVF, PQ), index architecture choices and performance tradeoffs, filtering strategies for hybrid search, distributed deployment patterns, benchmarking methodology, and scaling considerations from thousands to billions of vectors. Includes performance comparisons across Pinecone, Weaviate, Qdrant, pgvector, and Milvus.

LLM hallucination—generating confident but false information—is the primary reliability challenge in production AI applications. This guide covers the root causes of hallucination, detection strategies (fact-checking layers, self-consistency checks, confidence calibration), mitigation techniques (RAG, constrained generation, chain-of-thought verification), and monitoring approaches for production systems. Includes benchmark data on hallucination rates across different model and technique combinations.

LangChain Expression Language (LCEL) is the modern way to build composable LLM pipelines. This guide covers advanced LCEL patterns: parallel execution, streaming, dynamic routing, conditional chains, retry and fallback logic, tool use orchestration, and testing strategies. Includes production patterns for RAG applications, multi-step agents, and complex data transformation pipelines with real performance benchmarks.