Results for “model-selection”

Reference for building LLM-powered applications with the Claude API and Anthropic SDKs, covering model selection, parameters, streaming, tool use, and migration.

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI, Microsoft Agent Framework, GitHub Copilot SDK, ONNX Runtime, and OllamaSharp.

Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.

Recommends the optimal Claude model tier (Opus, Sonnet, or Haiku) for a given task by analyzing reasoning depth, blast radius, domain expertise, output length, and correctness cost, and suggests parallelization opportunities.

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology, including dataset creation, metric selection, failure analysis, and iterative improvement.

Guides a conversation to design a new AI team member, generating agent identity files and optionally setting up performance reviews.

Creates a declarative agent for Microsoft 365 Copilot by integrating an MCP server with authentication, tool selection, and configuration.