Data Preparation for Small and Open-Source AI Models

Technical manuals, archives, and internal records prepared for focused AI applications.

Focused applications depend on carefully selected knowledge. Equipment manuals, research papers, and internal procedures need readable text and reliable references so developers can assess their usefulness for the intended task.

Plan preparation around the intended use

Retrieval and RAG

Organize extracted passages with references to their original pages so application teams can test how well relevant information is located for user questions.

 

Fine-tuning

Define the training objective before selecting material. Extracted text may need additional cleaning, labeling, and review before it is suitable for the intended task.

 

Evaluation

Set aside representative examples before processing begins, keeping them separate from training inputs so developers can assess performance against a defined set of questions.

 

Collection documentation

Record where materials came from, what subjects and languages they cover, and how they were processed, including gaps that could affect their intended use.

 

Source identifiers

Attach agreed reference fields to each file so developers can reliably trace extracted content back to the relevant publication, edition, page, or original internal record.

 

OCR and document structure

Test representative pages containing tables, columns, and annotations to check reading order and extracted text before agreeing how remaining material should be processed.

 

Document preparation for open model teams

Before processing begins, establish which publications and internal records are approved for the intended application. Record any restrictions on access, adaptation, or redistribution alongside the project requirements.

Treat software licensing, content permissions, and release conditions as separate checks. Agree how sensitive files will be received, handled, and delivered.

A downloadable model does not automatically make an accompanying book, journal or scanned dataset open for reuse.

Private Document Collections for Focused AI Applications

A maintenance assistant may need equipment manuals, while a research tool may depend on a defined set of publications.

  • Identify the documents that answer the intended questions.
  • Distinguish current from superseded versions.
  • Separate material with different access restrictions.

Correct editions, equipment identifiers, publication dates, and references give developers concrete details to check during testing.

A useful brief for your first batch

Bring representative pages and a short description of the target task. Include:

  • The material’s condition, estimated volume and languages
  • Required fields and preferred file formats
  • Permitted uses and access restrictions
  • Challenging pages with tables, handwritten annotations, footnotes, or multiple columns

See AI Data Preparation for the full document process, or explore OCR and layout preservation in more detail.

Questions from model development teams

This service focuses on digitizing and preparing document collections. Your AI team remains responsible for model architecture, training, evaluation and deployment.

Not necessarily. A retrieval-based application can use relevant document content when responding to a question without retraining the model. The right approach depends on your task and system design.

Not necessarily. Access to weights alone does not establish the permissions or supporting materials associated with open-source AI. Review the actual model terms, and assess source-data permissions separately.

Only after your team has established the applicable permissions and addressed confidential or personal information. Do not assume that permission to digitize a collection also authorizes a public dataset release.