Suno AI Prompt Creation Story – How We “Wrote” a Texas Blues Album with AI
Can a machine have the blues? 🧐 It’s a question we playfully asked ourselves when embarking on the creation of “Texas Shuffle Inferno – Complete 97-Min Roadhouse Jam.” This 97-minute Texas blues album wasn’t written in the traditional way – there was no band in a studio jamming until 3 AM (though it sure sounds like it). Instead, it was composed by an AI music generator following our carefully written prompts. In this blog post, we pull back the curtain on the creative process behind the AI, detailing how we leveraged Suno AI to generate an entire Texas blues instrumental album. If you’re curious about AI music, prompt engineering, and the melding of human expertise with machine creativity, you’re in the right place. We’ll walk through the process step by step: from initial concept and research, to designing the prompts that guided the AI, to how the outputs were curated and polished into a cohesive album. Along the way, we’ll share the challenges we faced (yes, the AI had its diva moments, refusing to stick to structure at times!) and the solutions we discovered. By the end of this article, you’ll understand how the album was “written” – not by notes on paper, but by lines of descriptive text fed into an AI, resulting in authentic-sounding blues tracks. This story is as much about human creativity as it is about artificial intelligence: it took a blues-loving team armed with tech tools to teach a computer program what a Texas shuffle should feel like. So tune up your virtual Stratocaster and let’s dive into the prompt creation story behind Texas Shuffle Inferno. 🎸🤖
🛠️ Gear, Books & Links (affiliate)
- Fender Player Stratocaster – classic Texas-shuffle workhorse
- Ibanez TS-808 Tube Screamer overdrive pedal
- Texas Blues Guitar Icons – artist biographies
- Stevie Ray Vaughan – Texas Flood
- Fender Blues Junior Guitar Amplifier &
- Keeping It Bluesy: Ambiguous language could yield rockabilly riffs. Adding “shuffle” and specific scale keywords steered the AI back to pure blues.
- Length Constraints: We overcame the ~4-minute limit by stitching takes and generating bonus interludes.
- Dynamics: AI sometimes nailed crescendos and lulls, giving us goosebumps when it dropped to hush before a roar. Where static, we automated subtle volume changes.
- Timing Humanization: Most outputs swung nicely; a few felt metronomic, which we mitigated with ambience and mixing choices rather than manual note-shifting.
- Attribution & Rights: Under Suno’s policy for paid users, we own the audio. We credit “Composed by Suno AI” with us as producers in the album notes.
- Deep genre knowledge is vital. Detailed research into Texas blues informed more effective prompts.
- AI sparks new ideas. Unexpected outputs inspire future creative directions, like big-band blues or AI-written lyrics.
- Efficiency & scale. What once took months of studio time now took hours of prompt engineering and curation.
- Human touch remains vital. We guided, curated, and produced; AI provided the raw performance.
- Authenticity vs. originality. The album honors blues traditions without copying any specific song, offering fresh yet familiar improvisations.
Conception: Marrying Blues Heritage with AI Technology
The idea sprouted from a simple thought experiment: Could an AI be taught to compose a convincing Texas blues jam? As blues musicians ourselves (and tech nerds on top of that), we were excited and a bit skeptical. We knew that Texas blues has nuances – the swing in the rhythm, the emotion in a bent note, the interplay between instruments – elements that aren’t explicitly notated in sheet music but are felt. Capturing that in AI-generated music would be a challenge. But with recent advances in AI music generation, particularly Suno AI, we had a powerful tool at our disposal.
Suno AI is a platform that transforms text prompts into music. Think of it as “ChatGPT for music” – you describe the song you want, and the AI produces audio. Suno was developed by some very smart folks (alumni of Meta and TikTok) and is designed to let anyone create music, even if they can’t play an instrument. The system will consider genre, instruments, tempo, and even lyrics if provided, to generate an original piece.
As of the latest version (V3.5 at the time of our project), it’s capable of producing fairly long pieces – up to about four minutes per piece in length, a big improvement from earlier versions that could only do shorter clips. That 4-minute limit per generation is important; we’ll come back to how we handled track lengths and the album’s total runtime.
The next step was aligning this tech with deep research into Texas blues. Before writing a single prompt, we immersed ourselves in blues history and theory – effectively becoming AI musicologists. We identified four seminal Texas blues artists (spanning different eras) to serve as style anchors: in our case, we chose Blind Lemon Jefferson (representing early acoustic blues), T-Bone Walker (urban swing blues), Freddie King (electric blues instrumental and early rock crossover), and Stevie Ray Vaughan (modern blues-rock). Each of these artists has a distinct sound and legacy. We figured if we could get the AI to convincingly emulate aspects of each of these, we’d cover a lot of Texas blues ground.
We also listed signature elements of each artist’s style – almost like ingredients in a recipe. For example, for Stevie Ray Vaughan we noted: aggressive shuffles, loud Stratocaster through tube amps, fast pentatonic runs, and a driving rhythm section. For T-Bone Walker: swing rhythm, jazz chords, smooth phrasing, horns in arrangement. For Blind Lemon/Lightning Hopkins: minimal arrangement, acoustic guitar with alternating bass thump, improvisational structure, maybe ambient noises for realism. These notes later became the backbone of our prompts. Our goal wasn’t to copy any existing songs, but to generate new music inspired by those styles. We had to be careful to respect creativity and avoid just regurgitating any known melody (which AI sometimes risks if trained heavily on certain music). By focusing on “style” and “mood” in the prompts rather than any specific melody, we aimed to coax the AI into originality.
The Art of the Prompt: Writing the Blues in Text
Writing prompts for music generation turned out to be an art form in itself. We weren’t just scribbling “play a blues song” and hitting go. Instead, each prompt was a carefully crafted paragraph detailing multiple aspects of the desired track. Let’s break down the components we included in our prompts (and why):
Title & Short Description: We gave each prompt a sort of creative title or opener (for our own reference) like “Fiery Texas shuffle instrumental” or “Jump-blues romp.” This wasn’t necessarily parsed by the AI as a title, but it set the mindset for us. Sometimes we included it in the prompt text if we thought it might influence the style (like explicitly saying “Texas shuffle” to key it into that groove).
Instrumentation: This is crucial. We list the instruments we want, often with adjectives. For example: “1963 Stratocaster ➜ cranked Bassman + Tube Screamer, Fender Precision bass, Ludwig shuffle drums, faint Hammond B3 pad” was part of one prompt. This level of detail ensures the AI includes these elements. Naming specific gear (Bassman amp, Tube Screamer pedal) is our way of saying “give me that overdriven electric blues tone.”
Structure & Sections: We formatted sections in square brackets like [Intro], [Main Riff], [Solo], [Outro], etc. This was to guide the AI’s composition flow. By indicating sections, the AI tries to create a piece that has those parts in order. We also specified the total duration in the prompt (e.g., “Duration 3:45”). This is a hint to the AI how long to make the piece.
Tempo & Groove: We always included a BPM and feel. E.g. “132 BPM loose shuffle” or “70 BPM half-time shuffle.” This was essential because blues can vary from ultra-fast to extremely slow. The “feel” descriptor like “loose shuffle” or “12/8 feel” told the AI the rhythm type.
Key or Scale Emphasis: While not always specifying exact key (we did sometimes, like “Emphasis E minor pentatonic” or “G Dorian mode”), we often included the scale or mode. This guided the melodic content.
Mood & Production: Here’s where we set the scene. We used evocative words like “Mood: bold, sweaty roadhouse; warm room reverb, saturated tubes, subtle tape hiss” or “Mood: moonlit streetlamp glow, tape saturation warmth.” These phrases do two things: convey the emotional vibe and specify production elements.
No vocals note: We explicitly wrote “--instrumental only--” in each prompt. Since Suno AI can also generate vocals if lyrics are provided (and sometimes even if not, it might throw in a humming vocal), we needed to prevent that. That instruction successfully avoided any AI “la-la” or scat singing from appearing.
By combining all these elements, each prompt became a complete blueprint for a song. Here is one of the actual prompts we used for a Stevie Ray Vaughan-inspired track (we’ll annotate it in brackets):
Prompt Example: “Fiery Texas shuffle instrumental: 1963 Stratocaster ➜ cranked Bassman + Tube Screamer, Fender Precision bass, Ludwig shuffle drums, faint Hammond B3 pad. Structure [Intro] [Main Riff] [Solo] [Outro]. Duration 3:45. 132 BPM loose shuffle. Emphasis E minor pentatonic. Mood: bold, sweaty roadhouse; warm room reverb, saturated tubes, subtle tape hiss. --instrumental only--”
Reading that, you can almost imagine the song without hearing it, right? The AI certainly could. The output from this prompt was an exciting uptempo shuffle in E minor that kicks off with a drum fill (Intro), drops into a badass guitar riff (Main Riff), then goes into a long guitar solo with organ comping in the back (Solo), and closes with a definitive ending (Outro). The tone was rich and overdriven as specified, and you can even hear a bit of room ambience. When we first listened to it, we were floored – it was like “the AI gets it!”
We repeated this process for each of the 16 tracks, adjusting prompts for variety. Four prompts were aimed at Stevie-style electric blues (fast shuffle, slow blues, funky groove, and a heavy boogie). Four prompts targeted T-Bone/50s style (swing blues with horns, after-hours smoky bar vibe, uptempo jump blues, etc.). Four prompts for the early acoustic vibe (front porch stomp, talking blues with slide, country ramble, and a train shuffle). And four prompts for modern touches (like fuzz rock, or Gary Clark Jr. style blending).
The Generation Process: AI “Jam Sessions”
With prompts in hand, we fed them to Suno AI’s music generation model. We used the Suno web interface and, for longer pieces and more control, their Discord bot which allowed using the latest model that supports ~4 minute outputs. Each prompt generation took a couple of minutes of processing (pretty astounding – it’s composing in real-time almost!). We listened to each result carefully.
AI generation can have variability. Sometimes the first output is gold; other times it might miss the mark slightly. Suno often provides multiple versions or you can regenerate. For some prompts we got a great take on the first try. For a few, we did 2-3 generations and chose the best one. For example, one prompt came out too busy – the AI perhaps “overplayed” with the organ too loud stepping on the guitar. We tweaked the prompt (made sure to specify “faint organ”) and re-ran it, yielding a much better balance.
Another issue: endings. Some AI outputs just stop abruptly, or conversely, they kind of fade aimlessly if they weren’t sure how to end. We instructed [Outro] and often gave it a duration hint, which usually prompted a proper ending (like a resolving chord or drum fill to close). If an ending wasn’t satisfying, we might trim or even cross-fade into the next track if that felt natural.
To extend tracks beyond the 4-minute limit, we stitched together multiple AI takes or used “Replace Section” on pro features to refine specific parts without regenerating the whole track. We also generated short interludes like band warm-ups to add authenticity, ultimately creating about 97 minutes of content from 16 core tracks plus extras.
Post-Production: Polishing the Tracks
After generation, we had (give or take) 97 minutes of AI-produced audio. To make it truly sound like a cohesive album, we did some traditional post-production work:
Mastering levels: We normalized volume across tracks so one song wouldn’t be much louder or quieter than the next. A gentle compressor and limiter on each track evened them out.
EQ and tonal tweaks: We applied subtle EQ to ensure a consistent listening experience, for instance cutting boominess or taming harsh cymbals on certain tracks.
Sequencing and transitions: We arranged the tracks in a flow (electric front, acoustic middle, modern end), added reverb tails and ambient club chatter between tracks, simulating a roadhouse live set.
Editing out glitches: We removed tiny AI artifacts or abrupt cut-offs by smoothing fades or patching cymbal decays.
Stereo imaging: Minor panning and width adjustments ensured no element felt distractingly one-sided. We treated each track as part of a compilation album, aiming for unified sonic character.
Throughout post-production, we resisted the urge to overdub real instruments—our role was producer/engineer for an AI “band,” not to replace the AI’s output.
Challenges and Surprises
Working with AI had its learning curve and delightful surprises. Some notable points:
Delights included unexpected harmonized guitar/horn licks, piano fills on acoustic tracks, and highly realistic instrument timbres—each moment underscoring how sophisticated AI music has become.
Final Outcome and Reflections
We released Texas Shuffle Inferno – Complete 97-Min Roadhouse Jam on schedule, marking one of the first full-length Texas blues albums generated by AI. Key takeaways:
The project demonstrates a collaborative model where musicians become composer-directors, shaping virtual performances with words. Does the AI have the blues? It has as much as we give it—through our prompts, it found the soul of Texas blues and learned to sing it. 🎶🤖🎶
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