Lessons from photo restoration on how AI prompting skills matter more than the tech

A recent Reddit discussion about restoring the world’s oldest photograph has become an unexpected masterclass in the difference between thoughtful AI prompting and generic image generation. The comparison reveals why AI literacy—understanding how to effectively communicate with artificial intelligence systems—increasingly determines whether you get professional-quality results or what the community calls “AI slop.”

The photograph in question, captured around 1826-1827 by Joseph Nicéphore Niépce, a French inventor, represents a pivotal moment in visual history. Due to the primitive photographic technology of two centuries ago, the image appears grainy and unclear, making it an intriguing challenge for modern AI restoration tools.

However, the stark difference between two restoration attempts demonstrates that the quality of AI output depends heavily on the user’s approach, not just the technology itself.

The problem with generic AI requests

When one Reddit user simply asked ChatGPT to “restore and colorize the world’s first image,” the results were problematic. The AI system, lacking sufficient context and historical knowledge, essentially fabricated details. It added modern architectural elements, inaccurate colors, and even inserted a church that never existed in the original scene.

This represents a common pitfall in AI-generated content: when artificial intelligence systems lack adequate information, they fill gaps with plausible-seeming but incorrect details. For businesses relying on AI for content creation, marketing materials, or data analysis, this tendency toward fabrication can create significant credibility and accuracy issues.

A photography instructor who had studied the original image multiple times confirmed the AI’s errors, noting that major elements in the restoration were completely fictitious. This highlights how AI systems, despite their sophistication, cannot verify the accuracy of their creative additions without proper guidance.

The power of informed prompting

A second Reddit user demonstrated a dramatically different approach that yielded far superior results. Instead of providing a simple restoration request, they used what AI experts call “reasoning models”—more advanced AI systems designed for complex analytical tasks—and crafted a detailed prompt that acknowledged the image’s limitations.

The key difference lay in the prompt structure: “This is the world’s first image and I want you to colorize and restore the image. But the image itself may not have enough detail in it to recognize some details. Use the web and search for it to verify or fill in the blanks.”

This approach accomplished several critical things. First, it identified the specific historical significance of the image. Second, it acknowledged the AI’s potential limitations. Third, it directed the system to conduct research rather than rely solely on its training data.

The result was a restoration that, while not perfect, demonstrated significantly better historical accuracy and avoided the obvious fabrications of the first attempt.

Understanding AI reasoning models

The superior restoration used ChatGPT’s reasoning models, specifically o3 and o1-mini, which represent OpenAI’s more sophisticated AI systems designed for complex analytical tasks. Unlike standard ChatGPT, these models can break down problems into steps, conduct research, and verify information before generating responses.

For business applications, reasoning models offer particular value when accuracy and analytical depth matter more than speed. They excel at tasks requiring verification, multi-step problem solving, and integration of multiple information sources—capabilities that standard AI chat interfaces often lack.

However, these advanced models typically require more specific prompting techniques to achieve their full potential, making AI literacy increasingly important for professional users.

Practical applications for businesses

This restoration example illustrates broader principles that apply across business contexts where AI-generated content quality matters:

Marketing and branding: Companies using AI for visual content creation need detailed prompts that specify brand guidelines, target audiences, and accuracy requirements. Generic requests often produce content that appears professional but lacks brand consistency or factual accuracy.

Research and analysis: Business intelligence teams can achieve better results by directing AI systems to verify information, cite sources, and acknowledge limitations rather than simply requesting analysis.

Content creation: Publishers and content marketers benefit from prompts that specify fact-checking requirements, tone guidelines, and source verification rather than open-ended content requests.

A framework for better AI interactions

The successful restoration demonstrates a replicable approach for higher-quality AI results:

Provide context: Clearly identify what you’re working with and why it matters, rather than assuming the AI system understands the significance.

Acknowledge limitations: Explicitly state what information might be missing or unclear, directing the AI to research rather than fabricate.

Specify verification requirements: Request that the AI system check its work against external sources rather than relying solely on training data.

Use appropriate models: Choose reasoning-capable AI systems for tasks requiring accuracy and analysis, even if they take longer to process.

The broader implications

This photograph restoration case study reflects a larger trend in AI adoption: the growing importance of prompt engineering and AI literacy in determining output quality. As artificial intelligence becomes more prevalent in business operations, the ability to effectively communicate with these systems increasingly separates professional-grade results from amateur-level output.

Organizations investing in AI tools often focus on the technology itself while overlooking the human skills needed to maximize their effectiveness. The Reddit example suggests that training employees in effective AI communication techniques may be as important as selecting the right AI platforms.

For businesses evaluating AI adoption strategies, this comparison underscores the value of combining advanced AI capabilities with informed human guidance. The most impressive results emerge not from fully automated processes, but from thoughtful human-AI collaboration that leverages each party’s strengths while compensating for their limitations.