Artificial Intelligence (AI) is rapidly transforming the way we solve problems. However, one key realization from our journey is that AI alone is not a silver bullet. AI needs to be combined with other solutions and human expertise to be truly effective.
Through hands-on experience, we have learned valuable lessons about AI’s strengths, limitations, and the nuances of using it in real-world applications.
Here are six observations related to overcoming challenges that will help create a better understanding related to AI and respective challenges and opportunities:
1. Understand and Appreciate AI Beyond the Hype
Many people believe that AI can solve everything and expect highly precise results. However, in practice, AI’s accuracy depends on numerous factors, including data quality, model capabilities, and problem complexity. In most cases, developers need to supplement AI with additional solutions and algorithms to achieve reliable outcomes.
Over-reliance on AI can also lead to frustration, especially when results are inaccurate. If users don’t explore multiple sources and only depend on AI-generated outputs, they may struggle to find accurate insights. Our experience has reinforced the importance of treating AI as a powerful tool rather than an infallible solution.
2. Learn from Practical Applications
One of our key takeaways has been how different AI models serve different purposes. Large Language Models (LLMs) are excellent for text-based tasks, but they require improvements and collaboration with developers for more complex challenges. We have also realized that AI is much more than just LLMs and APIs—it includes machine learning, computer vision, and many other tools that complement each other.
Currently, AI assists greatly in coding and finding solutions, but errors still exist. It is crucial to validate AI-generated results with real-world testing. Through trial and error, we have gained a deeper understanding of how AI models work and how to optimize them for specific use cases.
3. A ‘One-Size-Fits-All’ Model Does Not Work for Video Analysis
One of the most complex applications we have worked on is video analysis. Making video content searchable and understandable for AI is a significant challenge. LLMs do not fully support video analysis with the level of accuracy required, so extracting useful information from videos requires a combination of multiple techniques, including:
- Transcription: Converting speech in videos into text for analysis.
- Frame Analysis: Identifying key moments in videos based on visual elements.
- Topic Detection: Understanding the context of a video through audio and image cues.
The difficulty lies in integrating multiple data sources to produce precise results. Different types of videos require different approaches—for instance, sports videos focus on actions, scores, and goals, whereas news videos emphasize speech, sound, and events. A one-size-fits-all AI model does not work; each domain requires specialized models and carefully crafted prompts.
4. Approach Video Segmentation Thoughtfully
Another major challenge is video segmentation—dividing videos into meaningful sections.
News and movies have clear scene transitions that make segmentation easier, but sports videos often have abrupt cuts, making it difficult to detect meaningful segments. Additionally, breaking a video at the wrong moment can lead to loss of context. For example, segmenting in the middle of a sentence disrupts the flow of a conversation, requiring a combination of image analysis and transcription processing to ensure accurate results.
5. Optimize and Fine Tune Prompts for Better AI Performance
Prompt engineering plays a crucial role in getting accurate AI outputs, but we have learned that prompts are only effective for predefined topics. A single prompt cannot cover everything—from sports to news to entertainment. Instead, fine-tuning prompts for specific categories significantly improves results. This process is ongoing, requiring continuous refinement and adaptation.
6. Moving Forward: Combine AI with Human Expertise
Currently, our team is focused on analyzing videos from various sources and optimizing prompts for different video categories. While AI provides powerful automation, human intervention is still necessary. We allow users to manually adjust results to enhance accuracy, bridging the gap between AI capabilities and real-world expectations.
Through this journey, we have gained invaluable insights into AI’s real-world applications. AI is an incredible tool, but it is not a standalone solution. It requires careful integration with other technologies and human expertise to achieve meaningful results.
As AI continues to evolve, so must our approach to using it effectively.
