Build a chatbot that answers from your PDF files

Choose PDFs that contain usable text

Start with current documents whose text can be selected and copied. Scanned pages, complex tables, diagrams, and multi-column layouts may need OCR or manual cleanup before any chatbot can retrieve them reliably.

Avoid uploading several versions of the same policy. If two files disagree, retrieval can surface either passage and the language model has no reliable way to know which version your business considers authoritative.

• Use descriptive filenames.
• Remove passwords and unnecessary personal data.
• Split unrelated topics into separate sources.

Understand what happens after upload

Knowvo extracts text, divides it into passages, creates embeddings, and stores searchable chunks. When a visitor asks a question, hybrid retrieval combines semantic similarity with keyword matches before passing relevant evidence to your selected model.

This is retrieval, not model training. Updating a source does not teach a new foundation model; it replaces the evidence available to the chatbot. That distinction makes corrections faster and keeps the source of an answer inspectable.

Validate citations and edge cases

Test exact figures, dates, exceptions, table entries, and questions phrased differently from the document. Open every citation and confirm that it contains the supporting text. A citation can identify the retrieved passage without proving that the generated sentence is fully accurate.

For scanned or visually complex PDFs, compare the extracted text with the original. If important information lives only in a chart or image, add a text explanation to a cleaner source rather than expecting the model to reconstruct the page layout.

• Ask five questions with no answer in the PDF.
• Confirm strict mode declines those questions.
• Retest after every important document update.