Most AI pipelines break in silence. The output “looks right” until it crashes everything downstream. This one was different. The pipeline crashed with a chorus of parsing errors, JSON mismatches, and broken promises.
Table of Contents
The Problem
I was brought in to fix a pipeline built with a popular no-code tool. It was intended to extract structured data from PDFs and store it in a vector database. Instead, the pipeline hallucinated, skipped fields, and crashed before anything was sent to the database. For most AI teams, hallucinated outputs and crashing pipelines spell disaster. For me, it’s just another Wednesday.
The creator of the pipeline assumed that requesting a JSON in the prompt would be sufficient. It didn’t. Without schema enforcement, AI invents fields, drops values, or outputs malformed JSON. The entire AI pipeline relied on fragile “The entire ouput MUST be ONLY the raw JSON object” prompt magic and a significant amount of luck. They were running out of both because the code was failing most of the time.
The Fix
Since the pipeline already relied on REST calls, I extended the architecture by replacing the brittle no-code section of model calling and output wrangling with a small, dedicated REST service. Because Docker was already part of their project stack, spinning up one more container was a non-issue.
In the service, I wrapped the model logic in a BAML function definition to enforce output structure, validate responses, and handle retries automatically. Then, I replaced a large chunk of the no-code pipeline with a call to a clean REST interface that fits right into the data formats used by the existing pipeline. The best part? It took less than one working day to implement.
The Results
The extraction accuracy jumped from “we don’t even know because it almost always crashes” to 95% with 100% correctness on structured fields. Only the summary generation was slightly off, but the client decided they don’t even need it. The pipeline, once unusable, is now production-grade. In one day.
You don’t need GPT-4 to fix hallucinations. In the end, we ran everything locally with Mistral 7B via Ollama. (Relatively) small models, big results.
AI doesn’t need to be magic. Sometimes you just need structured output and the right development process. Structure beats scale.
Schedule a 25-min call to see if your pipeline can hit 95 % correctness.
