APEX AI:
The Frontier Program
for Tech Leaders

The Gen AI landscape — GPT-4o, Claude 3.5, Gemini 2.0 & what each is best at

Prompt engineering for business roles — chain-of-thought, few-shot, structured output

AI ROI frameworks — measuring productivity gains for PMs, BAs & Architects

Enterprise AI adoption patterns — build vs buy vs fine-tune decision trees

LLM API patterns — function calling, streaming, tool use, JSON mode

RAG architecture — chunking strategies, vector embeddings, semantic search

Building internal knowledge copilots on company documents & wikis

AWS Bedrock, Azure OpenAI & Google Vertex AI — enterprise deployment options

PM playbook — AI-generated PRDs, user stories, sprint planning, and competitive analysis

BA playbook — requirements gathering automation, BRD generation, process mapping with AI

Architect playbook — AI-assisted architecture reviews, ADR generation, tech debt analysis

Test Manager playbook — AI test plan generation, test case coverage analysis, defect prediction

From chatbots to agents — what makes AI agentic: planning, memory, tool use

LangGraph architecture — nodes, edges, state graphs, and conditional routing

Tool-calling agents — web search, code execution, file I/O, API integrations

Human-in-the-loop (HITL) — approval gates, interrupt/resume, and escalation patterns

Multi-agent architecture — why one agent isn't enough for complex workflows

AutoGen — conversational agents, group chat, and code-execution agents

CrewAI — role-based crews, task delegation, and sequential vs hierarchical execution

Supervisor patterns — orchestrator + worker agents, conflict resolution, shared memory

LLM evaluation frameworks — RAGAS, DeepEval, and building custom eval suites

Hallucination detection — groundedness scores, faithfulness metrics, and guardrails

AI governance for enterprise — bias auditing, compliance (EU AI Act, SOC 2), risk tiers

AI testing automation — CI/CD pipelines for LLM regression testing and quality gates

Physical AI overview — AI in robotics, autonomous systems, and smart infrastructure

Edge AI fundamentals — NVIDIA Jetson, Raspberry Pi inference, model quantization

Computer vision at the edge — YOLO object detection, pose estimation, real-time video

Enterprise IoT + AI — sensor fusion, predictive maintenance, and digital twins strategy

Real-time AI architecture — event-driven AI vs batch processing trade-offs

Apache Kafka for AI — streaming data pipelines, partitioning, and consumer groups

Real-time inference serving — FastAPI + model caching, latency budgets, GPU batching

Production AI monitoring — drift detection, SLA alerting, observability with Grafana

Enterprise AI architecture patterns — microservices, AI gateways, model routing

AWS Bedrock vs Azure OpenAI vs Google Vertex AI — deep comparison for architects

AI security architecture — prompt injection defense, data privacy, API rate limiting

Cost optimization — token budgeting, caching, model cascade strategies for enterprise scale

AI-powered product strategy — roadmap generation, opportunity scoring with AI

Automated user research — AI synthesis of interview transcripts, survey analysis

AI requirements intelligence — extracting BRDs, use cases, and acceptance criteria from stakeholder conversations

Feature prioritization with AI — RICE scoring automation, competitive gap analysis

AI system design capstone — designing a production agentic AI platform end-to-end

AI-powered test automation — generating test cases, test data, and edge case discovery

CI/CD for AI systems — model versioning, A/B testing, blue-green AI deployments

Chaos engineering for AI — resilience testing, failure injection, and SLA validation

Capstone workshop — architecting your AI solution end-to-end (role-specific tracks)

PM/BA track — deploy AI Product Studio to a public URL, demo with real product scenario

Architect track — deploy agentic AI platform with monitoring, scaling & security hardening

Test Manager track — deploy AI QA pipeline integrated with CI/CD for a sample repo