Elevator Pitch
Stop fighting with generic AI code. Learn how “Context Engineering” transforms AI from a hit-or-miss chat bot into a specialized CFML expert. We’ll walk through the Research-Plan-Implement pattern to help you build, refactor, and document CFML apps faster and with fewer hallucinations.
Description
Does AI feel like a junior developer who doesn’t “get” your codebase? The secret to unlocking the power of LLMs isn’t just writing better prompts—it’s Context Engineering.
In this session, we move past the “magic trick” phase of AI and dive into practical, repeatable workflows designed for the CFML developer. Whether you are maintaining a 15-year-old legacy application or building a modern ColdBox service, the quality of your AI’s output is directly tied to the context you provide.
We will explore a structured Research → Plan → Implement pattern that ensures the AI understands your architecture before it writes a single line of code.
What you will learn: - The Context Gap: Why standard prompts fail and how to “prime” the AI with your specific CFML environment and database schema. - Context Management: How to feed your codebase to the AI effectively without hitting token limits or getting lost in the weeds. - The Workflow: A step-by-step demonstration of using AI to research a problem, draft a technical plan, and finally implement the solution. - Practical Automation: Real-world examples of using these patterns for refactoring, testing, and documentation.