From Unstructured Input to Evidence-Based Knowledge

I hate calling this "unstructured input" -- it's at least semi-structured. But the world beat me to it I guess!

No matter what you call it, automation starts with information. That information rarely arrives clean and organized. It's not "bad data" but it sure can be messy data!

A family recipe archive may contain photographs, handwritten notes, recordings, text messages, and test-cooking records. A useful AI workflow must preserve the original files, convert their contents into searchable text, add context, connect related entities, and retrieve the supporting evidence before generating an answer.

Think of the process as kitchen mise en place:

Transcription brings the ingredients into the kitchen.

Chunking separates them into workable portions.

Metadata labels the containers.

Embeddings group material by meaning.

Knowledge graphs record explicit relationships.

Retrieval selects the evidence needed for the current task.

The language model assembles the selected evidence, but human review determines what is accurate and what remains unresolved.

What does provenance look like in your automation systems? Can users trace an answer back to the original source?

https://creativecooking.blogspot.com/2026/06/tech-tuesday-input-to-structured.html

#Automation #AI #ProcessEngineering

1 comment