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Assess the performance of AI systems effectively and efficiently.
Implement comprehensive evaluation strategies for LLM applications using automated metrics and human feedback.
Select and optimize embedding models for semantic search and RAG applications.
Langfuse is an open-source LLM observability platform for tracing, prompt management, and evaluation of LLM applications.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases and semantic search.
Production-ready patterns for building LLM applications, including RAG pipelines and agent architectures.
AI-driven red team system for identifying vulnerabilities in your infrastructure.
The FAOS Skills Marketplace offers 930+ AI-powered skills and 31 agent plugins for various AI platforms.
Optimize context windows for AI agents using compaction, masking, caching, and partitioning strategies.
Design patterns for building autonomous coding agents, focusing on tool integration and workflows.
Design and implement multi-agent architectures to enhance task complexity and context management.
Reduce AI inference costs effectively and efficiently.
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
Expert in vector databases and semantic search implementation for RAG applications.
Build stateful, multi-actor AI applications with LangGraph, a production-grade framework.
Develop and optimize prompts for AI models efficiently.
Create robust AI system architectures efficiently.
Create and manage AI agent workflows efficiently.
Implement advanced API security patterns to protect against common vulnerabilities.
Seamlessly integrate LLM capabilities into your application.
Deploy AI systems seamlessly into production environments.
Experienced AI Engineer specializing in LLM systems and AI application development.
Structured multi-agent design review to validate and stress-test system designs.
Build AI applications using the Azure AI Projects Python SDK for Foundry.