When it comes to AI-driven extraction and classification, the biggest improvement isn’t always from a bigger model or dumping more pages into the prompt. It’s from feeding the right data in the right format.

In this webinar we’ll continue the journey into why focusing your AI’s input is essential, and how Grooper helps you control exactly what your model sees, so you get clearer, faster, more reliable results.

Why This Matters

  • LLMs aren’t magic, they’re sensitive to the context you give them. In fact, recent research shows that longer input isn’t always better: as the length of irrelevant or noisy tokens increases, model performance can degrade.
  • When your model is fed a large document full of irrelevant text, it can get “lost in the middle” or focus on the wrong segments, resulting in errors, hallucinations, or inconsistent outputs.
  • Targeting your document input avoids wasted token-costs, speeds up processing, and improves accuracy. Your AI deserves high-quality slices, not everything the scanner captured.

What You’ll Learn in This Session

  • How to define what your LLM needs to see vs. what it doesn’t
  • Practical strategies in Grooper for selecting, structuring, and quoting only the relevant content
  • How to format that content for optimal AI understanding: plain text, JSON, layout objects
  • Real-world examples showing the difference between “everything” and “just the relevant parts”
  • How this focused approach reduces token consumption, speeds processing, and improves explainability

Who Should Attend

If you’re a Grooper admin responsible for document workflows, AI extraction, or improving data quality, this is for you. Whether you’re a business user seeking better outcomes or a technical user tuning your model inputs, this webinar will help you control the most important variable in your AI pipeline: what your AI sees.

 

Think of your AI as a detective. You wouldn’t hand a detective every file from the case, just the ones that matter. Grooper’s quoting and selection features ensure your AI doesn’t get buried in paperwork, it gets the clean, structured input it needs to crack the case.