Most articles today focus on how users can write better prompts to generate more accurate AI-generated images as intended. But few ask a deeper question: What makes a generative AI model capable of understanding these prompts in the first place? The answer lies in the language understanding capabilities these models are built on, which are shaped by the quality of language data they are trained with. Descriptive, context-rich, real-world language data is what enables text-based generative AI models like DALL·E, Midjourney, and now GPT-4o Image Generation Model to “understand” what a cozy sunset looks like—or what “Ghibli-style” even means.
That’s where Flitto plays a key role—by providing high-quality language data that reflects how people actually speak, describe, and imagine the world.
Why Text-to-Image Generative-AI Models Need Better Language Data
Descriptive Data: Rich in Sensory Detail
Many prompt-writing guides emphasize the importance of rich, descriptive language to generate high-quality images. Effective prompts are often longer and packed with visual details so that the outputs do not end up generic or flat. Take, for example, a prompt like “draw a sunlit kitchen with warm wooden tones.” While AI models don’t truly understand language the way humans do, descriptive data helps them associate abstract words with visual details—allowing models to generate scenes that feel vivid and expressive.
That’s why it is important to train generative AI on language data that is not only grammatically correct, but also rich in sensory detail and aligned with how people naturally describe what they see and imagine.
Contextual Data: Culturally Grounded and Reflective of Real-Life Use
Words don’t exist in isolation. A well-crafted prompt often carries assumptions, tone, or context that goes beyond what’s explicitly written. For example, a prompt like “Ghibli-style train station at sunset” doesn’t just refer to a location or time of day. It implies a certain aesthetic mood and even a narrative. Without an understanding of what “Ghibli-style” evokes—soft lighting, nostalgic mood, hand-drawn textures—the AI model may produce an image that is visually correct but conceptually flat.
While large language models today can process massive amounts of text thanks to expanded context windows, that alone doesn’t guarantee “real understanding.” An AI model may technically “see” the full prompt, but without training on linguistic input that reflects how humans embed intent, it can still miss the point.
Human-Crafted Data: Created by People, Not Algorithms
Synthetic data is gaining traction as a scalable solution to data shortages in AI development. However, scalability often comes at the expense of quality. Data generated by AI models can be misleading or inconsistent, especially when it comes to certain industries or domain knowledge. Synthetic data may fail to capture the terminology or tone required for real-world use cases. For example, prompts related to healthcare, finance, or legal topics demand a level of specificity that only domain-aligned data can provide.
Data crafted by human, by contrast, reflects how people describe and interpret the world within specific domains, suggesting nuance, accuracy, and purpose. For generative AI to reliably understand and respond to sensitive prompts, it needs data built by people and associated with industry-specific meaning.
How Flitto’s Real-World Language Data Fuels AI to be Smarter
In Flitto Arcade’s “Listening” missions, key phrases are extracted from real-world utterances, such as flight boarding instructions or airport navigation queries. Then, “Speaking” missions instruct users to record naturally spoken sentences that include those phrases. This dual-layer approach not only captures how people speak in context, but also helps accumulate language data that reflect intent and circumstantial nuance.
In addition to collecting language data, Flitto also implements reliable evaluation process that help fine-tune generative models. One example is the Acceptability Rating Task, where human reviewers score multilingual sentence pairs based on meaning retention. This evaluation process captures how people interpret and judge language, an insight that is critical when training AI to understand prompts better. These human-scored data help bridge the gap between what AI can generate and what users find accurate and appropriate. Especially for text-to-image AI models, which rely heavily on natural language cues, this kind of human-validated feedback is essential to ensure the resulting visuals align with intent—not just keywords.
Flitto is building the linguistic infrastructure that makes generative AI models smarter, more responsive, and more human-aligned. Laying the foundation for how AI systems interpret richly descriptive prompts is what’s needed in the era of AI-generated images.
