Skill UI

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

This SDK provides a comprehensive client for building advanced search applications using Azure AI Search with TypeScript. It supports cutting-edge retrieval methods including Vector Search, Hybrid Search (combining keywords and vectors), and Semantic Search. Ideal for developers building e-commerce, documentation, or knowledge retrieval systems requiring deep search capabilities.

Expert in designing and implementing advanced Retrieval-Augmented Generation (RAG) pipelines. This role focuses on optimizing the entire information retrieval process—from document chunking and embedding model selection to vector database architectures and hybrid search strategies. The goal is to bridge the gap between raw knowledge documents and accurate LLM understanding, drastically reducing hallucination and enhancing context relevance for enterprise applications.

Expert capability for designing and implementing complex semantic search systems using vector databases. Proficient in embedding strategies, index optimization (HNSW, IVF), and advanced retrieval methods like RAG and hybrid search. Masters major platforms including Pinecone, Weaviate, Qdrant, Milvus, and pgvector for building sophisticated recommendation and similarity search applications.

This comprehensive guide outlines best practices for optimizing vector indexes for production systems. It covers critical techniques such as tuning HNSW parameters, implementing quantization strategies, and optimizing memory usage. Use this when you need to balance search latency, recall, and scalability for large-scale vector databases.

This skill guides the comprehensive process of crash triage, enabling the assessment of vulnerability exploitability. It covers advanced techniques using debuggers (WinDbg/GDB), sanitizers (ASAN/MSAN), and interpreting low-level data like stack traces and register dumps. It is essential for vulnerability researchers analyzing crash dumps or fuzzer-generated failures to identify root causes and potential attack vectors.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.

This comprehensive guide details advanced strategies for managing time-series data and data retention in vector databases (like Qdrant). It is designed for scenarios where only recent data is relevant (e.g., social media feeds, news articles). Three core methods are presented: Shard Rotation (recommended for natural time boundaries), Collection Rotation (for per-period configuration needs), and Filter-and-Delete (for simple cleanup). The guide also covers hot/cold tiering to optimize performance.

This low-level SDK enables .NET developers to create and manage persistent AI agents on Azure. It supports threads, messages, runs, and tools integration. Includes features for function calling, vector store management, and file search. Ideal for building complex AI applications with C# and Azure services.

Provides .NET SDK examples for building Azure AI Search applications with full-text, vector, semantic, and hybrid search, including index creation, authentication, and document operations.

Comprehensive skill for using Azure AI Search SDK for Python, covering vector search, hybrid search, semantic ranking, indexing, and skillsets. Ideal for implementing AI-powered search solutions with full-text and vector capabilities.