AI Skills Wiki
A bilingual wiki of the skills AI companies actually hire for — RAG, agents, evals, the modern stack, and enterprise compliance.
AI Engineering
RAGRetrieval-Augmented Generation (RAG)
Agentic AIAgentic AI & AI Agents
LLM EvalsLLM Evaluation (Evals)
Distributed TrainingDistributed Training for Large AI Models
LLM GuardrailsGuardrails for LLM Applications: Safety, Policy, and Runtime Controls
Inference OptimizationInference Optimization for Production AI Systems
Model ServingModel Serving: Deploying ML Models as Reliable Production APIs
Model Context ProtocolModel Context Protocol (MCP): connecting AI agents to tools and enterprise context
Agent orchestrationAgent Orchestration: Coordinating Tools, Memory, and Multi-Agent Workflows
Tool CallingTool Calling for AI Agents: Letting LLMs Use Functions and APIs
CUDA ProgrammingCUDA Programming for AI Engineers
RLHFRLHF (Reinforcement Learning from Human Feedback)
Synthetic DataSynthetic Data Generation for AI Systems
Model DistillationModel Distillation: Compressing Large AI Models into Deployable Systems
Vector Database OperationsVector Database Operations for Production AI Systems
Modern Stack
TypeScriptTypeScript for Senior Engineers
Modern ReactModern React (Hooks, Server Components, Next.js)
FastAPIFastAPI (Modern Python Backends)
pgvectorPostgres + pgvector (Vector Search)
KubernetesKubernetes for Application Engineers
Enterprise Security & Compliance
FedRAMPFedRAMP (US Government Cloud Compliance)
NIST FrameworksNIST Security Frameworks (800-53, CSF, AI RMF)
SOC 2SOC 2 Compliance for Engineers
OIDC / OktaOIDC, OAuth 2.0 & Okta (Enterprise Identity)
Audit LoggingAudit Logging (Enterprise-Grade)
Secrets ManagementSecrets Management for AI Systems
Prompt Injection DefensePrompt Injection Defense
AI Red TeamingAI Red Teaming