Build with AI: Vibe Code a Prompt Engineering Agent to Drive LLM Adoption

In this hands-on course, learn how to build a multiagent prompt engineering system that drives internal LLM adoption across your organization by automating dynamic prompt execution across complex workflows. Using tools like Bolt, Claude, and Airtable, instructor Lillian Pierson shows you how to build a chain of task-specific agents: a sequential pipeline where each agent executes specialized prompt engineering tasks with dynamic variable substitution. Create structured input interfaces, integrate LLM APIs with parameterized prompts, orchestrate multiservice automations, and implement file management systems. Build webhook handlers, automate Google Drive operations, and create database integrations for tracking outputs. Whether you're a data professional, tech leader, or AI strategist, this course equips you with the skills you need to scale prompt engineering best practices across departments, driving measurable enterprise AI adoption.

Learning objectives
Design a structured prompt input interface to help internal teams provide high-quality prompt components (task, context, and constraints) through a user-friendly front end.
Integrate with Claude, or any LLM API, to automatically generate optimized prompt variations based on structured user input.
Build a lightweight AI agent that responds to real-time triggers and autonomously performs prompt engineering tasks.
Create reusable workflow templates with parameterized prompts that can be duplicated and modified for different use cases across teams.
Learn methodologies to drive LLM adoption across enterprise business units by building automated prompt engineering workflows that reduce manual AI interaction complexity and enable consistent, repeatable outcomes through structured automation systems.