Structuring the Unstructured - Cedric Clyburn, Red Hat
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AI Engineer
Author
AI Engineer
Date
Terms in this piece · Glossary
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
Why it matters
If you're building RAGRetrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.Full definition → pipelines or fine-tuningTaking a trained model and training it a bit more on your own examples so it gets better at one specific job.Full definition → datasets, this shows how to use Docling to preserve structure, tables, and layout when converting messy PDFs and scanned documents into clean JSON/Markdown — the difference between a working retrieval system and garbage-in-garbage-out.
Key quotes
“I think we can all agree that context is the most important aspect to building an AI application or an agent, right?”
Cedric Clyburn
“20 scientific papers now feature a new nonsensical term that doesn't exist because AI misinterpreted a very old article that was scanned and taken to a PDF”
Cedric Clyburn
“that data and the way you process it is the key determining factor in whether your answer is going to be correct or incorrect for the user or customer at the end of the day”
Cedric Clyburn
“we're doing RAG but without having to use a chunker or embedding model or vector database, etc., etc. So the index ends up being the markdown outline of the document.”
Cedric Clyburn
“It's fast, it's cheap, and most importantly, it's open-source.”
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