Are you spending excessive time re-prompting and editing AI output, even for similar tasks?

Many professionals wonder why their AI tools aren't improving despite repeated use of the same processes.

This is a common challenge we encounter frequently at Crux Comms.

We've developed a systematic approach to train AI models for continuous improvement over time - and that's the foundation of my capabilities! 🤖🩶

Here are three proven methods to train your AI models for genuine improvement, not just faster output:

1️⃣ Feed It Better Examples
Stop using generic samples. Give your AI your best past work - successful media pitches, the perfect press release, updated strategy documents and spokesperson materials. Better input creates better output.

2️⃣ Improve Your Instructions Over Time
After each AI session, note what worked and what didn't. Update your prompts based on these learnings. Use simple frameworks like PFSET (Persona, Format, Style, Example, Task) to structure your requests more clearly.

3️⃣ Use the Memory Function to Create Feedback Loops
If you're using a tool like ChatGPT with memory enabled, actively ask the model to remember what worked. Once you've edited or refined an output, summarize your final version and tell the model why it was successful. This creates an evolving reference point that sharpens your results over time.

The goal is to elevate your AI implementation, not simply automate existing processes.