For developers building with LLMs, the magic is starting to wear off.
The ‘wow’ moment of getting a brilliant, unexpected answer in seconds has now been replaced by the engineering reality: How do you build a genuine, revenue-generating product on a foundation of unpredictability?
We see developers running into the same frustrations:
The hallucination bomb: An answer is 99% correct, but one invented ‘fact’ silently corrupts your database or misleads a customer.
The prompt-engineering puzzle: You spend days crafting the perfect prompt, only to have a single turn of phrase from a user break the entire chain.
The format surprise: You ask for clean JSON. You get a friendly paragraph with some JSON buried inside. Your error handling is now longer than your core logic.
The raw power of these models is not in question. But for any serious application – especially in fintech, where precision is currency – hope is not a strategy. The lack of control is a deal-breaker.
For developers building with LLMs, the magic is starting to wear off.
The ‘wow’ moment of getting a brilliant, unexpected answer in seconds has now been replaced by the engineering reality: How do you build a genuine, revenue-generating product on a foundation of unpredictability?
We see developers running into the same frustrations:
The hallucination bomb: An answer is 99% correct, but one invented ‘fact’ silently corrupts your database or misleads a customer.
The prompt-engineering puzzle: You spend days crafting the perfect prompt, only to have a single turn of phrase from a user break the entire chain.
The format surprise: You ask for clean JSON. You get a friendly paragraph with some JSON buried inside. Your error handling is now longer than your core logic.
The raw power of these models is not in question. But for any serious application – especially in fintech, where precision is currency – hope is not a strategy. The lack of control is a deal-breaker.
For developers building with LLMs, the magic is starting to wear off.
The ‘wow’ moment of getting a brilliant, unexpected answer in seconds has now been replaced by the engineering reality: How do you build a genuine, revenue-generating product on a foundation of unpredictability?
We see developers running into the same frustrations:
The hallucination bomb: An answer is 99% correct, but one invented ‘fact’ silently corrupts your database or misleads a customer.
The prompt-engineering puzzle: You spend days crafting the perfect prompt, only to have a single turn of phrase from a user break the entire chain.
The format surprise: You ask for clean JSON. You get a friendly paragraph with some JSON buried inside. Your error handling is now longer than your core logic.
The raw power of these models is not in question. But for any serious application – especially in fintech, where precision is currency – hope is not a strategy. The lack of control is a deal-breaker.
For developers building with LLMs, the magic is starting to wear off.
The ‘wow’ moment of getting a brilliant, unexpected answer in seconds has now been replaced by the engineering reality: How do you build a genuine, revenue-generating product on a foundation of unpredictability?
We see developers running into the same frustrations:
The hallucination bomb: An answer is 99% correct, but one invented ‘fact’ silently corrupts your database or misleads a customer.
The prompt-engineering puzzle: You spend days crafting the perfect prompt, only to have a single turn of phrase from a user break the entire chain.
The format surprise: You ask for clean JSON. You get a friendly paragraph with some JSON buried inside. Your error handling is now longer than your core logic.
The raw power of these models is not in question. But for any serious application – especially in fintech, where precision is currency – hope is not a strategy. The lack of control is a deal-breaker.