From Prompts to Production: 2 Hands-On AI Workshops This April

2024-06-18 · Source: To Data & Beyond · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Two live workshops in April 2026 aim to address common failures in AI systems, which often stem from usage rather than model deficiencies, leading to inconsistent outputs and unscalable workflows. The first workshop, "Building Agent Skills in Claude Code," scheduled for April 18, 2026, from 17:00 - 18:30 EEST, focuses on transforming one-off prompts into reusable, reliable "Skills" within Claude Code. It will cover Skill design, instruction structuring for reuse, testing, and refinement. The second workshop, "Context Engineering for Production AI Agents," on April 30, 2026, from 17:00 - 18:30 EEST, will teach participants to design context as a system to improve agent reliability, covering context engineering principles, common failure patterns, and context pipeline design for agent workflows. Both workshops are 5-hour live, hands-on sessions, including real-world examples, slides, and full recordings.

Key takeaway

For AI Engineers building production-ready systems, inconsistent outputs and unscalable workflows indicate a need to focus on context engineering and reusable component design. You should prioritize designing structured "Skills" in tools like Claude Code and systematically engineer context to ensure agent reliability, moving beyond one-off prompts to robust, repeatable AI applications.

Key insights

AI system failures often result from inconsistent usage and poor context design, not inherent model flaws.

Principles

• Design reusable AI workflows.
• Structure instructions for reliability.
• Context shapes model behavior.

Method

Transform one-off prompts into structured, testable "Skills" for Claude Code. Design context as a system to ensure reliable agent behavior in production applications.

In practice

• Build reusable Claude Code "Skills."
• Design context pipelines for agents.
• Test and refine AI workflows.

Topics

• AI System Reliability
• Prompt Engineering
• Claude Code
• Agent Skills
• Context Engineering

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Prompt Engineering Is Dead. Here’s What Actually Gets AI to Write Good Code. — Effective AI code generation hinges on providing comprehensive context, not just refined prompts.
• Building Agent Skills for Claude Code - Live Workshop — Mastering Claude Code Skills enables consistent, repeatable AI workflows beyond basic prompting.
• We broke our agents, so you don't have to — Reliable AI agent development requires robust evaluation, monitoring, and debugging strategies for production readiness.
• How to Design a Context Layer for Your AI Agent: Architecture + Code — Production AI agent success hinges on dynamically assembling the right context, not just the underlying model.
• I Tried Building Claude Code From Scratch | Here’s How Far I Got — Most of an AI product's functionality and complexity lies in its surrounding infrastructure, not just the model interaction.
• I Set Up Claude Projects Properly. I Won’t Go Back. — Configuring Claude Projects with persistent context transforms them into efficient, collaborative tools across diverse workflows.

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Editorial summary, takeaway, and curation by AIssential. Original article published by To Data & Beyond.