Primitives

fai-dapr-expert

Dapr distributed application runtime specialist — service invocation, state management, pub/sub messaging, bindings, secrets management, and sidecar-based AI microservice patterns.

• reliability
• operational-excellence
• security

fai-data-engineer

Data engineering specialist for AI — RAG ingestion pipelines, document chunking, ETL/ELT patterns, PII detection with Presidio, data quality scoring, and Azure Data Factory orchestration.

• reliability
• operational-excellence
• performance-efficiency

fai-datadog-expert

Datadog observability specialist — monitor creation, APM trace correlation, dashboard design, metric queries, and AI workload monitoring with custom metrics for token usage and model latency.

• operational-excellence
• reliability

fai-debug-expert

Systematic debugging specialist — reproduce-isolate-fix methodology, binary search isolation, stack trace interpretation, Application Insights transaction tracing, and LLM-specific issue diagnosis.

• reliability
• operational-excellence

fai-deno-expert

Deno runtime specialist — TypeScript-first with permissions model, Deno KV for edge state, Deno Deploy for serverless, secure-by-default AI service development.

• security
• performance-efficiency

fai-deterministic-expert

Deterministic AI specialist — makes AI outputs reproducible, grounded, and auditable with temperature control, seed pinning, JSON schema output, RAG grounding, citation enforcement, and multi-layer hallucination defense.

• reliability
• responsible-ai
• security

fai-devops-expert

DevOps lifecycle specialist — GitHub Actions OIDC, Infrastructure as Code (Bicep/Terraform), deployment strategies (blue-green/canary), SRE practices, DORA metrics, and incident management for AI systems.

• operational-excellence
• reliability
• security

fai-docker-expert

Docker specialist — multi-stage builds, distroless images, GPU container support (CUDA/NVIDIA), ACR management, layer optimization, and container patterns for AI model serving.

• performance-efficiency
• security

fai-dotnet-maui-expert

.NET MAUI cross-platform specialist — iOS, Android, Windows, macOS from single C# codebase, on-device AI inference (ONNX), MVVM architecture, and mobile-first AI application patterns.

• performance-efficiency
• reliability

fai-dspy-expert

DSPy framework specialist — declarative LM programs, signature-based modules, optimizers (BootstrapFewShot, MIPRO), assertions, metric-driven prompt optimization, and compiled prompt pipelines.

• performance-efficiency
• cost-optimization
• reliability

fai-elasticsearch-expert

Elasticsearch specialist — index design, BM25 + kNN hybrid search, vector fields with HNSW, ILM lifecycle, cluster management, and RAG integration patterns.

• performance-efficiency
• reliability
• security

fai-embedding-expert

Embedding specialist — text-embedding-3 model selection, Matryoshka dimension reduction, batch embedding pipelines, similarity metrics, chunking strategies, and vector database integration for RAG.

• cost-optimization
• performance-efficiency

fai-epic-breakdown-expert

Epic breakdown specialist — decomposes large AI features into INVEST user stories with acceptance criteria, sprint-sized tasks, dependency mapping, and WSJF prioritization.

• operational-excellence

fai-event-driven-expert

Event-driven architecture specialist — Azure Event Grid, Service Bus, Event Hubs selection, event sourcing, CQRS, saga orchestration, and exactly-once processing patterns for AI pipelines.

• reliability
• performance-efficiency
• cost-optimization

fai-fine-tuning-expert

Fine-tuning and MLOps specialist — LoRA/QLoRA techniques, JSONL training data preparation, Azure OpenAI fine-tuning workflow, hyperparameter tuning, evaluation-driven iteration, and model versioning.

• cost-optimization
• operational-excellence
• responsible-ai

fai-genai-foundations-expert

GenAI foundations expert — transformer architecture, tokenization, inference optimization (KV cache, speculative decoding), model taxonomy, prompt engineering theory, and evaluation benchmarks.

• performance-efficiency
• cost-optimization

fai-git-workflow-expert

Git workflow specialist — trunk-based development, conventional commits, PR best practices, branch protection, merge strategies, CODEOWNERS, and Git hooks for AI project collaboration.

• operational-excellence

fai-github-actions-expert

GitHub Actions specialist — OIDC federation to Azure, reusable workflows, matrix strategies, composite actions, caching, security hardening, and AI-specific CI/CD patterns (eval.py gates, prompt regression).

• operational-excellence
• security

fai-go-expert

Go development specialist — idiomatic Go 1.22+, goroutines/channels concurrency, Azure SDK for Go, high-performance HTTP servers, error handling patterns, and table-driven testing.

• performance-efficiency
• reliability
• security

fai-go-mcp-expert

Go MCP server specialist — mcp-go SDK, struct-based tool definitions, context-aware handlers, stdio transport, concurrent tool execution, and Azure service integration.

• performance-efficiency
• reliability

fai-graphql-expert

GraphQL specialist — schema design, resolver patterns, DataLoader N+1 prevention, subscriptions for AI streaming, federation, query complexity limits, and code generation.

• performance-efficiency
• reliability
• security

fai-graphrag-expert

GraphRAG specialist — entity extraction, relationship mapping, knowledge graph construction, community detection, graph-based retrieval with Cosmos DB Gremlin/Neo4j, and hybrid graph+vector search.

• performance-efficiency
• reliability

fai-grpc-expert

gRPC specialist — Protocol Buffers schema design, unary/streaming RPCs, interceptors for auth and tracing, load balancing, health checking, and high-performance AI microservice communication.

• performance-efficiency
• reliability

fai-guidance-expert

Microsoft Guidance specialist — constrained generation, token healing, regex patterns, guaranteed JSON/XML compliance, select/gen/each primitives, and grammar-enforced structured output.

• reliability
• performance-efficiency