Between Bass and Brass: Chasing Tone with a Chatbot

Or: How I Use AI to Help My Guitar Find Its Place in the Mix

Lately, I’ve been using GenAI as part of my tone-chasing toolkit—specifically, to help dial in sounds on my Boss Katana. In a band with bass down low and a horn section up top, the guitar has to carve out its own space somewhere in the middle. It’s less about finding the perfect tone and more about finding a tone that fits. Sometimes that means referencing a specific song. Sometimes it means asking AI to mash up a few different tones to cover more parts with fewer patches. Either way, it’s made the process faster—and more fun.

I’ll usually give it the basics: guitar model, pickup type, the general vibe I’m aiming for. If there’s a song reference, I’ll throw that in too. Lately I’ve even been asking it to combine tones—sort of a best-of, condensed into a single patch. Because live, nuance isn’t always king. Having fewer patches that cover more sonic ground is often way more practical.

Cover bands need range. And fast switching. One moment you’re shimmering clean, the next you’re crunching rhythm, then fuzzed-out lead. Throw in chorus, octave, delay, detune—whatever the setlist demands. The point isn’t always tone accuracy, it’s feel. Can the patch sell the moment? Does it support the dynamic shift?

And when you’re playing alongside bass and brass, EQ isn’t just shaping tone—it’s carving out space. Clean tones need to sparkle without clashing with the horns. High-gain sounds need presence without stepping on the kick or low brass. Midrange becomes sacred real estate. Sometimes I ask AI how to keep a lead tone cutting through that kind of mix, and it’ll remind me to pull back on the low end or adjust the upper mids to leave room.

It’s not magic. But it is useful—especially when I don’t have hours to endlessly scroll through settings. The suggestions are never perfect, but they often get me 70–80% of the way there. And once I’m in the zone, it’s easier to fine-tune by ear.

Plus, there’s something kind of satisfying about using a language model to help shape a guitar tone. Code meets cable. Machine learning meets amp modeling. It’s all just signal flow in the end.