Challenges of building an LLM product: a Chrome extension for English writing improvement
Building LLM products for production requires navigating unreliable API uptime and latency, unpredictable prompt engineering, hallucinations, a lack of evaluation metrics for outputs, and API endpoint deprecation that can force costly prompt rebuilds.
The team built a full set of crafted, fine-tuned, few-shot prompts for OpenAI's DaVinci-002 model, which began working well, but OpenAI then deprecated that endpoint — described as not an easy transition.
The post recommends best practices — finetuning over API prompts for complex cases, few-shot and chain-of-thought prompting, vector databases for large data, and versioned prompts — but reports no quantified outcome for the Chrome extension itself.
Frequently asked questions
What did this team achieve with this AI workflow?
The post recommends best practices — finetuning over API prompts for complex cases, few-shot and chain-of-thought prompting, vector databases for large data, and versioned prompts — but reports no quantified outcome f…
What tools did this team use?
OpenAI API, DaVinci-002.
What results were reported?
Prompt performance after fine-tuning: started working really well (source-reported, not independently verified).
What failed first in this deployment?
The team built a full set of crafted, fine-tuned, few-shot prompts for OpenAI's DaVinci-002 model, which began working well, but OpenAI then deprecated that endpoint — described as not an easy transition.
How is this workflow AI workflow structured?
Non-native speaker triggers extension → Few-shot prompts sent to LLM API → Natural language output returned.