Suno for Filmmakers: A 30-Day Creator Case Study — LiliDi Blog
We chronicle one filmmaker's journey over 30 days, exploring how Suno AI integrated into their workflow and truly benefits filmmakers—without the hype.
By lilidi editorial
Suno for Filmmakers: A 30 Day Creator Case Study When new AI tools emerge, the initial excitement often outpaces practical application. Suno AI, a platform generating music from text prompts, arrived with significant buzz, promising to revolutionize how creators approach soundtracks. But for filmmakers, an industry steeped in intricate detail and often tight budgets, the question isn't just "Can it make music?" but "Can it make useful music, consistently, without costing more in revisions than it saves?" This article chronicles the first 30 days of a creator named Alex, an independent filmmaker specializing in short narrative content and documentaries, as they integrated Suno AI into their existing workflow. Alex wasn't looking for a magic bullet but a practical tool to address specific pain points in their audio post production process. This isn't a review written by an AI evangelist;
it's a grounded look at how Suno performed in a real world creative context. Week 1: Initial Exploration and Realistic Expectations Alex's primary goal for Suno was to generate placeholder scores and royalty free background music for explainer videos and documentary segments. Full orchestral scores demanding specific emotional arcs were intentionally set aside for later exploration, if at all. The focus was on utility. Day 1 3: Prompting Basics and Genre Experiments Alex started with straightforward prompts, testing various genres and moods. Early attempts revealed the importance of specificity. Initial Prompt: "Upbeat acoustic folk music." Result: Often generic, sometimes with unexpected instrumentation. Revised Prompt: "Melancholy piano jazz, slow tempo, late night cafe ambiance, instrumental only." Result: Noticeably closer to the desired mood, though still requiring generation of
several tracks to find a suitable loop. The key takeaway was that Suno, like any AI, benefits from detailed and evocative prompts. Simply stating a genre wasn't enough; specifying instrumentation, tempo, mood, and even location imagery significantly improved outputs. Day 4 7: Identifying Production Utility By the end of the first week, Alex identified Suno's immediate utility: generating royalty free background music for non critical segments. For instance, a short corporate explainer video needed a "light, optimistic corporate jingle, 30 seconds." Suno produced several usable options, saving Alex the time of sifting through stock music libraries. Wins: Quick generation of ambient and generic background tracks. Challenges: Achieving highly specific emotional resonance or complex melodic structures remained elusive. Week 2: Iteration and Workflow Integration Building on the initial
experiments, Alex delved into refining prompts and integrating Suno into an actual project: a short documentary about local artisans. Day 8 14: Customization and Looping One of the critical needs for filmmakers is music that can be edited and looped seamlessly. Suno's "Continue from this song" feature became invaluable. Alex would generate an initial 30 second segment that was close to the mark, then use the continuation feature to extend it. This wasn't always perfect, often introducing slight variations, but it provided more raw material to work with in the editing suite. Prompt for documentary segment: "Warm, reflective acoustic guitar, gentle rhythm, evokes a sense of craftsmanship and tradition, no vocals." Process: Generate 30s. Listen. If promising, continue. Repeat until a 2 minute segment was available for trimming. Alex noted that while Suno could produce good "starts,"
consistency over longer durations was still a challenge. Manual editing in their DAW (Digital Audio Workstation) was often required to smooth transitions or create perfect loops. The "lilidi.ai" Connection It's worth noting that while Suno focuses on audio, the visual elements generated by platforms like lilidi.ai can sometimes inspire musical prompts. A particularly evocative image from lilidi.ai of a misty forest, for example, might lead to a Suno prompt like "Ethereal, ambient electronic music, slow tempo, sounds of distant birds and gentle rustling, melancholic." This cross pollination of AI tools opens up interesting creative avenues, reinforcing how different AI platforms can complement each other to build a complete artistic vision. Wins: Efficient generation of longer, thematically consistent tracks (with some manual refinement). Challenges: 'Perfect' looping still often required
manual audio editing. Week 3: Addressing Specific Scene Needs Alex began tackling more specific scene requirements, pushing Suno beyond basic background scores. Day 15 21: Underscore and Foley Attempts Could Suno generate music to underscore a specific emotional beat? A dramatic reveal, for instance. Alex found that by breaking down the emotional need into granular musical terms, the results improved. Initial Prompt: "Dramatic reveal music." Result: Often over the top or generic "movie trailer" sounds. Revised Prompt: "Sparse strings, building crescendo, deep cello emphasis, slow tempo transition to lighter, hopeful piano melody." Result: Significantly better, though still requiring multiple attempts to get the timing and emotional arc just right. The "hopeful" part was particularly challenging to convey consistently. Foley generation was also explored. While Suno is primarily for music,
Alex experimented with prompts like "Gentle rain sounds, distant thunder, for a quiet forest scene." The results were hit or miss, often incorporating musical elements. Dedicated sound effect libraries or recording remained superior for specific foley. Wins: Ability to approximate basic emotional underscores with meticulous prompting. Challenges: Fine tuning emotional arcs and generating precise foley sounds remained difficult. Week 4: Efficiency, Licensing, and Future Prospects The final week focused on overall efficiency gains, understanding licensing, and future integration. Day 22 26: Time Savings and Cost Efficiency Alex estimated that Suno saved approximately 5 10 hours over the 30 day period, primarily by reducing time spent browsing stock music libraries or attempting to compose simple placeholder tracks from scratch. For a solo filmmaker, this efficiency gain is significant. The
cost of a monthly Suno subscription was easily offset by the time saved, especially for projects with limited budgets where custom composition was out of reach. Day 27 30: Licensing and AI "Style" Concerns Alex carefully reviewed Suno's licensing terms, confirming that commercial use was permitted for tracks generated under their paid subscription. This clarity is crucial for filmmakers. A subtle concern emerged: a slight "AI sound" or consistency across different generations, which, while not a deal breaker for background music, might make it identifiable if overused across multiple projects. This is where platforms like lilidi.ai aim for authenticity in their image generation, and a similar pursuit of indistinguishable quality is an ongoing challenge for AI music platforms. Wins: Demonstrable time and cost savings for specific types of musical needs. Clear commercial licensing.
Challenges: A subtle "AI fingerprint" in some generations. Not a replacement for a human composer for complex, unique scores. Conclusion: A Tool, Not a Replacement After 30 days, Alex concluded that Suno for filmmakers is a valuable tool , not a comprehensive replacement for a human composer. It shines in situations requiring: Placeholder Tracks: Quickly sketching out mood or rhythm during the edit. Background Ambiance: Royalty free music for explainer videos, documentary montages, or scene transitions. Budget Constrained Projects: Providing accessible music options where custom scores are financially unfeasible. For unique, emotionally rich, and meticulously crafted scores, human composers remain indispensable. Suno excels at the utilitarian, freeing up time and resources for filmmakers to focus on other critical aspects of their craft. Its utility lies not in replacing creativity, but
in augmenting efficiency. FAQ Q: Can Suno create full length movie scores? A: While Suno can generate extended tracks, creating a cohesive, emotionally intricate, and thematically consistent full length movie score across multiple scenes remains highly challenging. It's better suited for shorter segments and specific cues rather than an entire orchestral narrative. Q: Is the music generated by Suno truly royalty free for commercial use by filmmakers? A: Yes, under Suno's paid subscription tiers, the music you generate is typically licensed for commercial use. Always refer to Suno's official terms of service for the most up to date and specific licensing information. Q: How does lilidi.ai compare to Suno for creative pursuits? A: lilidi.ai and Suno serve different creative domains. Suno specializes in AI music generation, while lilidi.ai focuses on AI image and video generation. They are
complementary tools, with visuals from lilidi.ai potentially inspiring musical prompts on Suno, and vice versa, rather than directly comparable platforms. Both aim to empower creators, but in distinct media types. Related on LiliDi How LiliDi compares to Suno