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With the wave of AI-generated content (AIGC), more and more people are falling into a certain obsession: thinking that by “Prompt Polishing”, that is, continuously refining prompts, they can get large models (LLM) to generate better, more creative, and more original content. However, is this obsession really worth it? In trying to elevate AI like ChatGPT to human creative levels, we may have overlooked a simpler, more direct idea: utilizing AI to filter and uncover high-quality original works that already exist but are buried.
Rather than putting effort into Prompt Polishing, trying to squeeze inspiration from AI, we might as well view AI as a powerful filter to help us discover already created but unnoticed highlights. This is the more practical and valuable use of AI at this stage.
The Originality Issue of AIGC: The GIGO Curse
“Garbage In, Garbage Out”, this phrase is particularly applicable in the world of AIGC. Even the most advanced large language models will produce unsatisfactory results if the input data quality is low. Many people hope to obtain stunning creativity from AI by optimizing prompts, but in reality, they are merely piecing together stories that look decent from data fragments.
Current AI in content creation is still “stacking-type”; it relies on a large amount of existing information and does not have the ability to create new concepts from scratch. Many hope to produce an engaging short story through polished prompts, but the results often lack soul and are monotonous. Instead of wasting countless hours on such attempts, it is better to use AI as a tool to uncover existing yet undiscovered excellent works.
Buried Excellent Originals: Niche Novels and Ignored Voices
The internet is a vast ocean of content, and many excellent original works are buried like pearls in the sand. For instance, novels published on niche forums that have not gained widespread readership; or in-depth blog posts and podcast episodes buried in the flood of information. These contents may not be suitable for commercial bestsellers, but they offer unique perspectives and thoughts that deserve wider visibility.
Imagine a science fiction author posting an imaginative short story on an inconspicuous forum, but due to a lack of traffic support, it receives very few reads. These works may be filled with creative sparks. Instead of letting AI piece together stories, why not let AI dig out these forgotten pearls and present these shining works of human wisdom to more readers through more effective recommendation mechanisms?
Similarly, the rise of podcasts in recent years has allowed more people to express themselves through sound, but many excellent podcast contents are overlooked due to information overload. Since audio content is hard to browse quickly, the reading cost is high, leading to some deep and creative podcast content being undiscovered by the public. By using AI to transcribe audio and analyze its essence, we can certainly find interesting viewpoints and stories that deserve to be heard by more people and match them with the right audiences.
The Best Use of Current AI: Replacing Workload, Not Creativity
At this stage, AI excels not in creativity but in efficiency. The strength of AI lies in handling repetitive, mechanical, and high-load tasks, saving creators time and energy. In other words, AI is good at clearing dust rather than building souls; its value does not lie in reinventing stories but in finding forgotten chapters. Letting AI do what it is good at, such as content classification, topic mining, voice transcription, and basic editing, can significantly improve work efficiency. True creativity and inspiration still need to be provided by humans.
Many people try to use AI to write novels, but the results are often exquisite yet hollow text assemblies, seemingly smooth but lacking soul. In contrast, if we treat AI as an “assistant editor”, allowing it to help us find potential works online, organize, summarize, and extract overlooked good content, then humans can create and refine it, this combination may be more likely to produce true masterpieces.
From Polishing to Filtering Pearls: Practical Strategies for Using AI to Unearth Originals
So, how can we specifically use AI to discover and promote original content?
Suppose you are an independent developer running a tech blog. Instead of trying to generate new technical tutorials with AI, you might as well use AI to search for existing but buried technical articles and tutorials. For example, you can use AI tools to scan GitHub README files, unearthing some content-rich but underappreciated open-source projects, then reorganize them into meaningful articles. This not only makes your content more authentic and credible but also helps you build a unique content library.
Similarly, in the podcasting field, you can use AI tools to batch transcribe podcasts in the field and then let AI help analyze the similarities and highlights of viewpoints to summarize some overlooked industry trends. For instance, by using tools like Descript or Whisper, we can transcribe podcast audio content into text, and then use AI models to analyze the core themes and unique perspectives in the content. Through this approach, your content sources will be richer, and your audience will become more loyal due to the unique insights you bring.
In terms of social media content, AI can also become an efficient filter. In this era of information explosion, truly deep content may be quickly drowned due to low interaction. Through AI’s natural language processing technology, we can filter out long articles that, although they have low interaction, possess unique insights, reorganizing them for new dissemination.
Collaboration Between Creators and AI: The Cycle of Filtering and Secondary Creation
We need to change our mindset: at this stage, letting AI replace creation is less effective than letting AI replace filtering and organizing. The inspiration for creation comes from human emotions and experiences, which AI cannot replace, but filtering and organizing is where it excels. Through this method, we can more effectively extract precious content that humans have already created, and then proceed with secondary creation and dissemination.
This collaboration between creators and AI is not a one-sided tool use but a relationship of mutual cooperation and leveraging each other’s strengths. In the process of AI helping you filter content, you not only save tedious search time but also accumulate an understanding of industry content through this collaboration, gradually forming your own original style. In this cycle, AI is a booster, not the source of creativity.
Discovering and Promoting Originals with AI: Stop Obsessing Over Prompts
Current AI still lacks the ability to create truly deep content from scratch, but it can become a powerful tool for us to discover and disseminate high-quality content. Instead of consuming time and energy on Prompt Polishing, we should view AI as a potential content filtering tool, helping us find those buried original works and assisting them in reaching more people’s visibility.
So, stop obsessing over Prompt Polishing. At this stage, the greatest value of AI lies not in replacing human creativity but in replacing inefficient tasks, allowing us to discover, organize, and disseminate truly valuable original content more quickly. For every independent creator and super individual, finding and spreading originality is the more meaningful thing at this stage.
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