I’m working on a project that needs to turn plain text descriptions into JSON format for automation workflows. The JSON will be used in workflow tools or sent to APIs for execution.
There are so many different ways to do this and I’m getting confused about which one to pick. I want to make sure I choose the right approach before building anything.
Here are the methods I’ve found:
Basic prompting - Just asking an AI model nicely to create the right JSON
Multiple agents - Using different AI agents for different parts like planning, creating, and checking
RAG systems - Giving the AI examples and context to work with
Custom training - Teaching a model using my own examples of text and JSON pairs
OpenAI JSON format - Using OpenAI’s special JSON mode
Function calls - Making the AI use predefined function templates
Instructor library - Converting AI responses into validated Python objects
PydanticAI - Similar to Instructor but with more features
I need high accuracy and the JSON must follow a complex schema that changes over time. The system should be flexible for future updates.
My main questions are:
Which method works best for reliable text-to-JSON conversion?
How can I test this without building a full prototype?
When does each approach work better than others?
What are the pros and cons for speed, scaling, development time, and reliability?
Has anyone built something similar or have tips for converting natural language to structured data?
honestly, i’d go with function calls + pydantic validation. been using this combo for 6 months and it handles schema changes really well. openai’s json mode is nice, but function calls give you way more control over structure. quick tip - start simple and add complexity gradually. don’t try solving everything at once.
I’ve hit the same issues building automation pipelines. The Instructor library with structured prompting works best for complex schemas - it’s way more consistent than other approaches I’ve tried. I’ve learned that reliability comes from validation layers, not just better prompts. The Instructor automatically enforces your schema and provides proper error handling when the model generates faulty JSON, saving me tons of debugging time.
For testing without building full prototypes, create a small validation dataset with edge cases and run batch evaluations. Testing multiple approaches on the same dataset helped me compare accuracy; for instance, basic prompting failed on 15% of complex nested structures, while the Instructor dropped that to under 3%.
One crucial aspect concerning evolving schemas - no matter what approach you choose, ensure you can version your schema definitions. I learned this the hard way when a schema change broke production. Versioned schemas allow you to migrate existing automations gradually without disruptions.
I’ve built similar systems and here’s what worked best for me: OpenAI’s JSON mode with strict schema validation. Got the sweet spot between reliability and speed when I built a document processing pipeline last year. Consistency beats perfect accuracy every time. I use a two-stage setup: AI generates the JSON first, then a separate validator checks it against your schema and catches problems. When validation fails, the system retries automatically with the error details fed back into the prompt. For testing without building full prototypes, I made a synthetic dataset using GPT-4 to generate varied text descriptions, then manually verified the expected outputs. Let me benchmark different approaches super quickly. Basic prompting worked fine for simple stuff but completely fell apart on nested structures. JSON mode with schema hints in the prompt dropped my error rate from 12% to 2%. For evolving schemas, embed version numbers directly in the JSON output. Makes migration tracking way easier when you need to update workflows. One gotcha - the biggest performance bottleneck isn’t the AI processing, it’s the validation step. Optimize that early.