technology-selection

name	technology-selection
description	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. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
license	MIT

.NET AI and Machine Learning

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.

Step 1: Classify the task (decision tree)

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.

Step 1b: Pick the library layer

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.

Step 2: Cover the branch essentials, then decide depth

Every answer — plan or implementation — must address the guardrails for the selected branch:

• ML.NET — new MLContext(seed: …) (reproducible); TrainTestSplit + evaluate on the held-out set; report real metrics (MicroAccuracy/MacroAccuracy/LogLoss, AUC/F1, or RMSE/R²); serve with PredictionEnginePool<TIn,TOut> (never a singleton PredictionEngine).
• LLM (MEAI) — depend on IChatClient registered via AddChatClient (provider behind it); set Temperature and MaxOutputTokens in ChatOptions; add retry/timeout (RetryingChatClient/Polly); pin a dated model; load keys from user-secrets / env / Key Vault — never hardcode an sk-… key; validate non-deterministic output against a schema with a fallback.
• Agentic (Agent Framework) — orchestrate with Microsoft.Agents.AI on IChatClient (never a hand-rolled loop); set MaximumIterations and a token/cost ceiling; define each tool with a clear schema (AIFunctionFactory.Create); log each step (never raw sensitive content).
• RAG / embeddings — semantic chunking (not fixed-size); IEmbeddingGenerator and cache the embeddings (don't re-embed per query); store/query with Microsoft.Extensions.VectorData.Abstractions (MEVD) + the provider the user asked for (e.g. pgvector); filter by a minimum similarity score; keep source attribution for each answer. Honor the UI/storage the user specified; use only real, existing NuGet packages.

Then choose depth:

• Plan / comparison / architecture only (or "do not write code"): answer from this file alone using the essentials above. Do NOT open a reference — the branch essentials here are sufficient for a selection or plan. For RAG plans, cover chat, ingestion/chunking, embeddings, vector storage, source attribution, and the requested UI/storage.
• Writing implementation code: read the matching reference(s) for packages and implementation guidance (read only the selected branch; for Hybrid, read both Classic ML.NET and LLM):
  • Classic ML.NET → references/classic-ml.md
  • LLM integration (MEAI) → references/llm.md
  • Agentic (Agent Framework) → references/agentic.md
  • RAG / embeddings / ingestion → references/rag.md
  • GitHub Copilot extensions → references/copilot.md
  • ONNX Runtime inference → references/onnx.md
  • Local/offline LLM with Ollama → references/ollama.md

Validation

• Selection follows the decision tree — no LLM for tasks ML.NET handles
• Only what was asked is produced (plan-only requests get a plan, not code)
• AI/ML services registered via DI; config via IOptions<T>; keys from secure sources
• Branch guardrails (Step 2 essentials, plus the reference when implementing) are satisfied
• After implementing, build and run existing tests

Anti-Patterns to Reject

Anti-pattern	Redirect
LLM for tabular classification	Use ML.NET — faster, cheaper, deterministic
LLM calls without retry/timeout	Add RetryingChatClient or Polly retry
API keys in committed appsettings.json	user-secrets / env / Key Vault
Accord.NET, or defaulting to Semantic Kernel without a requirement	ML.NET; prefer MEAI + Microsoft.Agents.AI for new work
Hand-rolled multi-step tool loops with IChatClient	Microsoft.Agents.AI (MaximumIterations, tool dispatch)
Agent Framework for a single prompt→response	IChatClient directly
Raw HttpClient/OpenAI SDK in business logic alongside MEAI	one abstraction layer; depend on IChatClient
PredictionEngine singleton in ASP.NET Core	PredictionEnginePool<TIn,TOut> (not thread-safe)
RAG without chunking or relevance filtering	semantic chunking + minimum similarity score
Building custom neural nets in .NET from scratch	pre-trained via ONNX Runtime or an LLM API