1 - What is RAG and Why Every Developer Should Know It
Discover what Retrieval-Augmented Generation (RAG) is, how it works, and why it's the most important technique for integrating AI into modern web applications.
AI per Sviluppatori Web: RAG, LLM e Agents
Guida pratica all'integrazione di AI nelle applicazioni web: RAG con vector databases, LLM API, AI agents, semantic search e chatbot intelligenti per sviluppatori frontend e backend.
8Articles
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Titres et sous-titres
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Chapitres
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Annexes
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Bibliographie160 minLecture totaleIntermedioNiveau
AIRAGLLMagentsvector databaseweb development
Articoli de la Série XPHX0XPHX Les articles de la série XPHX1XPHX sont les suivants :
- 1
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2 - Implementing RAG with TypeScript and LangChain.js
Practical guide to implementing a complete RAG system using TypeScript, LangChain.js, embeddings and vector stores.
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3 - Vector Databases for Web Developers
Complete guide to vector databases: what they are, how they work, comparison between Pinecone, ChromaDB, Weaviate and Qdrant for AI applications.
- 4
4 - Integrating OpenAI and Anthropic APIs in a Web App
How to integrate OpenAI (GPT-4) and Anthropic (Claude) APIs in TypeScript web applications: authentication, streaming, error handling and best practices.
- 5
5 - Local LLMs with Ollama: AI Without Cloud
How to run LLM models locally with Ollama: installation, configuration, TypeScript integration and comparison with cloud APIs.
- 6
6 - Fine-tuning vs RAG: When to Use What
In-depth comparison between fine-tuning and RAG: costs, performance, use cases, and a decision framework for choosing the right approach.
- 7
7 - AI Agents: Architecture and Implementation Patterns
How to design and implement AI Agents: ReAct architectures, tool calling, memory management, and patterns for multi-agent systems.
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8 - AI in CI/CD: Intelligent Pipeline Automation
How to integrate AI in CI/CD pipelines: automatic code review, test generation, intelligent security scanning and assisted deployment.
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