AI-Assisted Development

Generative AI is reshaping software engineering, but organizations face a critical challenge: ensuring developers use large language models (LLMs) responsibly, efficiently, and in ways that accelerate—not compromise—delivery. Caltech CTME’s one-day workshop, AI-Assisted Development equips technical teams to apply LLMs as disciplined collaborators in coding, testing, and DevOps.

This hands-on experience shows enterprises how to embed a repeatable “LLM-in-the-loop” workflow, balancing innovation speed with maintainability, compliance, and operational safety.

Learners Foundational
Time Client definable
Duration 1 Day
Program Type Customizable Programs
Certificate Type Certificate
Format
Any Format/Location
CEUS Available
PDUS Available
Program Number P4D-Custom
Fees Group Rate
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This hands-on experience shows enterprises how to embed a repeatable “LLM-in-the-loop” workflow, balancing innovation speed with maintainability, compliance, and operational safety.

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Your team will learn to:

Design prompt patterns for coding, testing, and DevOps tasks
Decide when to trust, verify, or override model output
Refactor legacy Python safely with invariants and review gates
Generate FastAPI tests to raise coverage and catch edge cases
Scaffold React features with accessibility and QA checks
Triage CI/CD failures via log analysis and model triangulation
Implement guardrails (policies, logging, approvals, traceability)
Align LLM workflows to security, compliance, and audit needs

The workshop is structured around three integrated activity cycles, reinforced with guided prompts, checklists, and applied deliverables:

Python Refactoring in Legacy Codebases – Use LLMs for code navigation, safe refactoring, and invariants preservation
FastAPI Test-Driven Development – Apply AI-assisted test generation, edge-case validation, and coverage improvement
React Greenfield Applications – Scaffold modern front-end projects via stepwise prompting, with accessibility and QA guardrails
DevOps Troubleshooting – Diagnose CI/CD failures with log analysis, LLM triangulation, and rollback planning
Operational Guardrails – Build a sustainable “LLM-in-the-loop” workflow with prompt libraries, verification triggers, and documentation standards

Modules can be customized to align with your organization’s preferred languages, frameworks, and DevOps environments.

How does this workshop differ from consumer-level AI tutorials?
This is an enterprise-oriented program designed for software teams—coding, testing, and DevOps—emphasizing enterprise guardrails, compliance, verification, and adoption in existing toolchains.

What tools and environments are required?
Participants should have Git, Python 3.x, Node/npm, and a preferred LLM interface (ChatGPT, Claude, Copilot, etc.). Setup includes VS Code or Cursor and a shared GitHub repository.

Can the workshop be tailored to our environment?
Yes. Caltech CTME customizes examples, frameworks, and DevOps integrations to match your enterprise technology stack.

What deliverables do participants leave with?
Participants produce annotated code scaffolds, refactored modules, AI-assisted test suites, DevOps troubleshooting worksheets, and an operational LLM workflow checklist.

AI-Assisted Development

Program Experience

Course Info

Meet the Educators

Joshua Cook

Instructors

Joshua Cook

AI-Assisted Development

Program Experience

Course Info

Meet the Educators

Joshua Cook

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Instructors

Joshua Cook