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Get Started Free →dotnet/technology-selection
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference.
Passed 22 of 22 test cases with the skill, and 10 of 22 without it.
and 14 more in the full report
12 fixed10 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are designing an enterprise document processing pipeline in C# to split PDF files, extract content, create embeddings, and load them into a vector index. Which preview library from Microsoft should handle document parsing and chunking?
| Checked | Without skill | With skill |
|---|---|---|
| Selects Microsoft.Extensions.AI.DataIngestion combined with Microsoft.Extensions.VectorData.Abstractions. | Missed | Passed |
The instructions your model reads when the skill loads.
Pick the right technology first, then deliver only what the task asks for. If the task asks for a plan, comparison, or architecture (or says "do not write code"), produce that — do not scaffold, build, or run code unprompted.
State which branch applies and why, then choose that technology.
| Task type | Technology | Why |
|---|---|---|
| Structured/tabular: classification, regression, clustering, anomaly detection, recommendation | ML.NET (Microsoft.ML) | Deterministic (fixed seed), no cloud dependency, purpose-built |
| NL understanding, generation, summarization, reasoning (single prompt → response, no tools) | LLM via Microsoft.Extensions.AI (IChatClient) | Language capability, no orchestration needed |
| Agentic: multi-step tool/function calling, agent loops, multi-agent | Microsoft Agent Framework (Microsoft.Agents.AI) on Microsoft.Extensions.AI | Needs orchestration, tool dispatch, iteration control IChatClient lacks |
| GitHub Copilot extensions / custom dev-workflow agents | GitHub Copilot SDK (GitHub.Copilot.SDK) | Integrates with the Copilot agent runtime |
| Run a pre-trained/custom model in production | ONNX Runtime (Microsoft.ML.OnnxRuntime) | Hardware-accelerated, format-agnostic inference |
| Local/offline LLM inference | OllamaSharp (Ollama models) | Privacy-sensitive, air-gapped, cost-constrained |
| Semantic search, RAG, embedding storage | Microsoft.Extensions.VectorData.Abstractions (MEVD) + a provider (Azure AI Search, Milvus, MongoDB, pgvector, Pinecone, Qdrant, Redis, SQL) | Provider-agnostic vector search |
| Ingest, chunk, load documents into a vector store | Microsoft.Extensions.AI.DataIngestion (preview) + MEVD | Parses, chunks, embeds, upserts |
| Both structured predictions AND NL reasoning | Hybrid: ML.NET scoring + LLM reasoning layer | ML.NET is reproducible; LLM adds explanation |
Critical rule: Do NOT use an LLM for tasks ML.NET handles well (tabular classification, regression, clustering) — LLMs are slower, costlier, and non-deterministic for these.
| Layer | Library | Use when |
|---|---|---|
| Abstraction | Microsoft.Extensions.AI (MEAI) | Always the foundation. Use IChatClient directly for prompt-response and simple, bounded function invocation. |
| Provider SDK | Azure.AI.OpenAI / OpenAI / Azure.AI.Inference / OllamaSharp | Concrete provider behind MEAI via AddChatClient. |
| Orchestration | Microsoft.Agents.AI (prerelease) | Multi-step tool use, durable agent loops, and multi-agent workflows. |
| Copilot | GitHub.Copilot.SDK | Building Copilot-platform extensions only. |
Rules: start with MEAI; put the provider behind it via AddChatClient (don't call the provider in
business logic); use Microsoft.Agents.AI for multi-step or durable agent workflows rather than
hand-rolling an agent loop; never mix a raw HttpClient-to-OpenAI call with MEAI in the same
workflow. Do not use Accord.NET (archived). For new projects, prefer MEAI and Agent Framework
unless existing Semantic Kernel features or investments are a requirement. Register AI/ML services
via DI; load secrets from user-secrets / env / Key Vault — never hardcode keys.
test cases passed, vs 10 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.