Hey folks! I’m working on a cool project where we want to turn plain English descriptions into JSON for automating workflows. It’s for n8n, if you’re familiar with that tool. We’ve got a bunch of ideas on how to do this, but we’re not sure which way to go.
We’re looking at stuff like using AI prompts, multi-agent setups, and even some fancy techniques like RAG and fine-tuning models. There’s also OpenAI’s JSON mode and function calling, plus some Python libraries like Instructor and PydanticAI.
The tricky part is we need it to be super accurate and follow a complex schema that might change over time. We’re trying to figure out:
- What’s the best way to build this?
- How can we test it without building the whole thing?
- Are some methods better for certain situations?
- What about things like how fast it runs, how easy it is to build, and how reliable it is?
Has anyone done something like this before? Any tips or experiences you can share? We’re pretty excited about this project but could use some guidance. Thanks!
I’ve tackled similar challenges in my work. From my experience, a multi-agent setup combined with fine-tuning can yield impressive results for complex schemas. We used this approach for a client project last year.
The key is to break down the task into smaller, specialized agents. One agent handles initial parsing, another manages schema validation, and a third deals with edge cases. This modular approach allowed us to iterate and improve each component independently.
For testing, we created a comprehensive test suite with various edge cases and ran it against a scaled-down version of our system. This helped us identify potential issues early on.
Regarding performance, we found that while this method was more resource-intensive initially, it significantly improved accuracy and adaptability to schema changes over time. The trade-off was worth it for our use case.
Remember, thorough documentation and regular review cycles are crucial for maintaining such a system long-term.
hey there! i’ve done smthing similar before. my advice? start simple with openai’s json mode - it’s pretty good for accuracy. you can test it by running small batches and checking the output manually. speed might be an issue, so keep that in mind. good luck with ur project!
Having worked on similar projects, I’d recommend a hybrid approach. Start with OpenAI’s JSON mode for a baseline, then incorporate RAG to handle schema changes. This combination offers flexibility and accuracy. For testing, create a diverse set of sample inputs and expected outputs. Validate against these before full implementation. Consider factors like latency and cost when scaling. Regarding maintainability, document your process thoroughly and build in error handling. Remember, perfect accuracy is challenging; plan for human review of edge cases.