Separate ingestion health from retrieval readiness
A failed ingestion run should not stop retrieval while an accepted index remains queryable and eligible to serve.
Ai-Engineering
Cap tool-call loops explicitly
Set a hard iteration limit on automatic function invocation and treat hitting it as an explicit application outcome.
Embeddings in .NET
How to generate text embeddings with Microsoft.Extensions.AI and keep the model, dimensions, comparison metric, and indexing behavior compatible.
What Happens When Your AI Feature Fails?
A practical way to identify failure modes, choose explicit application behavior, and test an AI feature before its successful path hides the hard decisions.
Designing an AI Service Layer
How to place model access, prompts, retrieval, tools, and application policy behind a maintainable use-case boundary in a .NET application.
Validate tool arguments at the execution boundary
Treat model-generated tool arguments as untrusted input even when they satisfy the generated schema and bind successfully to the expected C# types.
Trust Boundaries Around AI Features
How to keep model proposals separate from trusted application behavior with authorization, approval policy, current-state checks, and least privilege.
Why Retrieval Exists
Why application knowledge does not belong in model weights, what retrieval contributes, and when a prompt, database query, or deterministic tool is simpler.
The Model Is Only One Dependency. Map the Rest.
How to map the execution, prerequisite, and operational dependencies around an AI feature and give each interaction a usable failure contract.
Stop Letting Provider SDKs Define Your .NET AI Architecture
How Microsoft.Extensions.AI keeps provider SDKs at the edge of .NET applications through shared abstractions, dependency injection, and middleware.