Overcoming The Experimentation Bottleneck
Code generation has become one of the most impressive and accessible use cases for LLMs but the ease of generation isn't the same as product differentiation. As AI-native teams explore faster ways to build, the enduring challenge is not building but learning what to build.
Low-code tools are going mainstream
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Multilingual NLP will grow
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Combining supervised and unsupervised machine learning methods
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Automating customer service: Tagging tickets and new era of chatbots
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Detecting fake news and cyber-bullying
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Code generation has been one of the most successful use cases for LLMs since the debut of chatGPT. Many providers offer a base model optimized for coding, and many benchmarks used to evaluate LLMs emphasize coding skills.
Some are reporting 10X'd output by "vibe-coding" with AI assistants. It's now possible to write functional code without fully understanding the underlying logic—describe what you want, and the model works out the implementation.
Mistakes occur, particularly when you prompt LLMs to write code that is very different from the training data. However, coding agents are already testing code in sandboxed environments and feeding tracebacks into debugging loops. Some even predict the commodification of high-quality code within the year. Fast feature rollout? That's table stakes now. But velocity doesn't guarantee value.
As your teams learn to tame the space of potential new candidate features, the same old challenge of discovering what matters to your users remains. We know from the collective experimentation histories of many industry leaders that most "great ideas" will fail to impact business or user outcomes meaningfully.
That's why when my friends ask about moats in AI, I tell them not to over-index on models or data. Foundation models have become commodified, and data generation is cheaper than ever.
To make a defensible AI product, your team must manage the content overload, trying to integrate millions of possible artifacts with the firehose of new methods and frameworks. Efficient search for the best way to bring it together to delight your users requires knowledge discovery.
Put differently; your team must overcome the experimentation bottleneck to improve consistently by implementing high-quality evaluation and testing.
More organizations will apply AI in business reasoning and in ways that directly impact user experience. Your moat will not lie in your collection of static artifacts but in your company's culture of experimentation, which helps you continuously improve.
Agile AI engineering with an integrated development and experiment platform.
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