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Generative AI Bootcamp

Build with modern GenAI: prompts, agents, evaluation, and safe deployment.

8 weeks·Hybrid·Cohort format

Overview

A cohort-based program focused on practical generative AI: LLM APIs, retrieval, tool use, evaluation, and lightweight agent patterns. You will ship projects that look like what teams build today, not toy demos.

Who it's for

  • Developers adding GenAI to existing products
  • Engineers preparing for AI application roles
  • Builders who want structured evaluation and safety habits early

What you will learn

Curriculum breakdown, from foundations to portfolio-ready work.

Phase 1

Prompting & APIs

Patterns that survive real usage.

  • Prompt design, structured outputs, and failure cases
  • OpenAI-style APIs, streaming, and cost awareness
  • Caching, batching, and observability basics
Phase 2

RAG & knowledge systems

Grounded answers with measurable quality.

  • Chunking, embeddings, and retrieval strategies
  • Re-ranking and hybrid search concepts
  • Evaluation sets and regression testing for answers
Phase 3

Agents & deployment

From script to maintainable service.

  • Tool-calling patterns and guardrails
  • Simple agent workflows with clear boundaries
  • Capstone: GenAI feature with eval harness

Key features

  • Hybrid live workshops + on-demand modules
  • Hands-on labs with real API workflows
  • Mentor feedback on architecture and prompts
  • Assignments with rubric-based grading
  • Interview-style system design for AI features

Projects

RAG assistant

Document-grounded Q&A with citations and a small eval suite.

Tool-using workflow

A constrained agent that calls tools safely with logging and retries.

Outcomes

Skills gained

Ability to design, evaluate, and iterate GenAI features responsibly.

Job readiness

Talk confidently about retrieval, eval, and failure handling.

Portfolio

Deployable demos with clear documentation and metrics.

Your mentor

Jordan Park

Staff Applied AI Engineer

10+ years shipping ML and GenAI products

Jordan focuses on pragmatic GenAI architecture: what works in production, what breaks, and how to measure it.

RAGEval harnessesProduct integration

Duration & schedule

Total duration

8 weeks

Weekly commitment

10–14 hours

Cohort format

Weekly live deep-dives; async project work with milestone reviews.

Ready to join the next cohort?

Secure your seat or request the full syllabus. We'll confirm prerequisites and start dates.

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