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AI & Machine Learning

LLM integration, RAG, prompt engineering, and AI agent development

10 skills
★ Featured

LLM API Integration

Best practices for integrating LLM APIs with error handling, streaming, and cost management.

Claude CodeCursorGitHub Copilot
★ Featured

RAG Implementation Patterns

Retrieval-Augmented Generation with chunking, embeddings, and hybrid search.

Claude CodeCursorGitHub Copilot
✓ Recommended

AI Agent Development

Building AI agents with tool use, planning, memory, and safety patterns.

Claude CodeCursorGitHub Copilot
★ Featured

Claude API & Anthropic SDK

Claude API integration with Messages API, streaming, tool use, vision, and extended thinking.

Claude CodeCursorGitHub Copilot
✓ Recommended

OpenAI API Best Practices

OpenAI API with chat completions, function calling, embeddings, and fine-tuning patterns.

Claude CodeCursorGitHub Copilot
✓ Recommended

LangChain & LangGraph Orchestration

LangChain and LangGraph for building AI chains, agents, and multi-step workflows.

Claude CodeCursorGitHub Copilot
✓ Recommended

Vector Database Patterns

Vector databases with Pinecone, Qdrant, pgvector, and Chroma for similarity search and AI applications.

Claude CodeCursorGitHub Copilot
✓ Recommended

AI Safety & Responsible AI

AI safety patterns including content filtering, bias detection, guardrails, and responsible deployment.

Claude CodeCursorGitHub Copilot
★ Featured

MCP Server Development

Model Context Protocol server development with tool definitions, resource management, and transport patterns.

Claude CodeCursorGitHub Copilot
✓ Recommended

Prompt Engineering Techniques

Prompt engineering with chain-of-thought, few-shot, structured output, and evaluation patterns.

Claude CodeCursorGitHub Copilot