Models seem to be able to do anything these days, but I am curious if you see any cases where it doesn't work and can't seem to match up or hit a wall.
The benchmarks show a pretty picture, but the real-world performance in areas such as healthcare, legal analysis, finance, and scientific research has not been objectively quantified.
So, I am curious about which domain your work is in, and within your workflows, where does the current AI fall short?
Hi everyone 👋
I’m Priyanka, the maker of DataCreator AI.
I built DataCreator AI after spending years dealing with a frustrating reality in AI development: collecting and preparing high-quality datasets often takes more time than building the models themselves.
Most tools focus heavily on prompts and models, but data quality is still one of the biggest bottlenecks in AI.
So I decided to build a platform focused specifically on helping developers, researchers, and AI engineers create better datasets faster.
Here are some things you can do with DataCreator AI:
🌱 Generate synthetic datasets for training, fine-tuning, and evaluation workflows.
🌱 Export datasets in CSV, JSON, and JSONL formats for AI/LLM pipelines.
🌱 Create structured datasets for conversational bot training, tool calling datasets, eval datasets, instruction tuning, classification, summarization, and more.
🌱 Review, clean, and enhance generated outputs to improve dataset quality with the help of a quality report.
🌱 Add context from PDFs and web search to generate customized datasets.
🌱 Use DataCreator AI Python SDK to embed data generation into your existing workflows.
Coming Soon:
✨ Higher number of data points per generation.
✨ More file formats like SQL.
✨ Anything else you mention in the comments.
Any feedback is welcome and highly appreciated.
Velo
Congrats on the launch @priyanka_madiraju , last year I had to spend an entire weekend scraping for data for training an NLP model. This seems like a true solution to it