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Build a local-first inference gateway with exact cache keys, per-tenant budget reservation, sync-vs-batch routing, deferred execution, and measurable cost controls in C#.

Build a local-first prompt experiment harness with frozen eval cases, deterministic scoring, live LM Studio execution, and evidence-based prompt promotion.

Build a local-first inference gateway with deterministic rate limiting, queue backpressure, retries, timeouts, circuit breakers, fallback routing, and measurable runtime reliability metrics.

Build a local-first memory architecture with deterministic write guardrails, bounded retrieval, context budgeting, and evidence-only answers.

Build a deterministic context budgeting pipeline that prioritizes required evidence, enforces token limits, and composes inspectable prompts.

Use structured JSON inputs and outputs to make LLM systems predictable with typed parsing, validation, and deterministic routing.

Learn the core AI pattern for semantic retrieval with embeddings as representation, deterministic vector ranking, thresholded control, and optional pgvector scaling.

Build a local-first incident triage agent with Microsoft Agent Framework using structured workflows, deterministic policy enforcement, and explicit runtime guardrails.

Spec-driven AI-assisted coding treats specifications as execution contracts, constraining agent behavior for predictable, production-grade outcomes.

Build a local-first .NET intent router that keeps routing, tools, and safety deterministic while using embeddings only for disambiguation under controlled thresholds.