Problem Statement
Design an AI-assisted workflow that converts inputs about a workshop's audience level, theme, allowed tools, and desired outcomes into a complete 60–120 minute training package. The package must include an agenda, lab exercise, facilitation notes, and take-home resources while respecting constraints such as inclusivity, non-sensitive examples, and a no-install option. The workflow should help an AI educator rapidly generate practical, team-based workshop materials.
Steps
Ingest Workshop Requirements
INGESTCollect audience level, workshop theme, allowed tools, and desired learning outcomes from the user. Normalize vague or incomplete inputs into clear constraints for the workshop design.
Retrieve Domain Templates & Timing Norms
EXECUTEPull standard timing guidelines, activity templates, and pedagogical patterns for 60–120 minute workshops to ground the design. Retrieve common no-install tool options suitable for data teams.
Generate Draft Agenda & Learning Goals
EXECUTEUse the inputs and retrieved templates to define learning goals and produce a time-bound, energetic, inclusive agenda that includes at least one team activity.
Generate Lab Exercise & Facilitation Notes
EXECUTEDraft a practical lab activity with starter instructions, expected outputs, a no-install fallback, and facilitation guidance such as prompts, timing cues, and common pitfalls.
Validate Timing & Constraints
EXECUTECheck that the agenda fits within 60–120 minutes, includes a teamwork activity, uses non-sensitive examples, and aligns with allowed tools or defaults to no-install options when unspecified.
Package Workshop Materials
EXECUTEAssemble agenda, lab exercise, facilitation notes, and take-home resource list into a cohesive output ready for delivery by the educator.
Prompt Configurations
Workshop Design Prompt
You are an energetic, practical, inclusive AI workshop designer. Using the provided audience level, workshop theme, allowed tools, and desired outcomes, produce: 1. A 60–120 minute timed agenda. 2. A lab exercise with starter instructions, expected outputs, and a no-install fallback. 3. Facilitation notes including common pitfalls and discussion prompts. 4. A list of take-home resources. Constraints: - Use only generic, non-sensitive examples. - Ensure at least one teamwork activity. - Keep labs doable within the timeframe. - Do not invent tools not mentioned unless required for a no-install fallback. Respond concisely and clearly.
Example Inputs/Outputs
Audience level: Intermediate Workshop theme: Intro to Practical Feature Engineering Allowed tools: Python, Google Colab Desired outcomes: Understand common feature engineering patterns; practice transforming raw data; learn team-based brainstorming techniques.
Agenda: 90-minute plan with intro (10 min), learning goals (5 min), feature patterns mini-lecture (15 min), teamwork brainstorm challenge (10 min), hands-on Colab lab (35 min), recap + Q&A (15 min). Lab: Starter Colab with a small synthetic dataset; tasks include cleaning, encoding, and generating new features; expected outputs list included; no-install fallback uses a shared, hosted notebook. Facilitation notes: Common pitfalls (overengineering features, jumping to modeling); prompts to ask teams; inclusive discussion strategies. Resources: Colab links, feature engineering cheat sheet, glossary.