How do I search PDFs and slide decks with AI?

You can search PDFs and slide decks with AI in a few practical ways, depending on how many files you have and how precise you need the search to be.

1) Use an AI tool that can ingest files directly

Many assistants let you upload:

• PDFs
• PowerPoint slides (.pptx)
• sometimes images/scans

Then you can ask questions like:

• “Find every mention of customer churn.”
• “What does the deck say about pricing?”
• “Summarize the section on quarterly revenue.”
• “Which slide discusses risks?”

Good for:

• a handful to dozens of documents
• ad hoc exploration
• quick summaries and Q&A

2) Use “semantic search” instead of keyword search

Traditional search looks for exact words. AI search can find meaning even if the wording differs.

Example:

• Search: “employee offboarding policy”
• It can also find: “termination procedures,” “resignation workflow,” “exit checklist”

This works by:

1. extracting text from the PDF or slides
2. splitting content into chunks
3. creating embeddings for each chunk
4. searching by similarity to your question

3) Build a document Q&A system with RAG

If you want to search a library of docs, the common AI pattern is Retrieval-Augmented Generation (RAG).

Basic flow:

1. Extract text from PDFs/slides
2. Chunk text into small sections
3. Create embeddings
4. Store them in a vector database
5. On query, retrieve the most relevant chunks
6. Give those chunks to an LLM to answer with citations

Common tools:

• Extraction: PyMuPDF, pdfplumber, Apache Tika, Unstructured
• Slides: python-pptx, Unstructured
• Embeddings: OpenAI embeddings, sentence-transformers, Cohere
• Vector DBs: FAISS, Chroma, Pinecone, Weaviate, Elasticsearch
• Frameworks: LlamaIndex, LangChain

4) OCR for scanned PDFs and image-heavy slides

If the PDF is scanned or the slides are screenshots, you need OCR first.

Options:

• Tesseract
• Google Document AI
• Azure Document Intelligence
• Amazon Textract
• Adobe OCR

Without OCR, the AI may miss the text entirely.

5) Search by page or slide with citations

For best usability, return:

• document name
• page number or slide number
• extracted quote/snippet
• confidence/relevance score

Example result:

• Q: “Where do they mention GDPR?”
• A: “Slide 12 discusses GDPR compliance in the data retention section.”

6) If you want the simplest setup

For a quick workflow:

• upload the PDF/deck to an AI assistant that supports file chat
• ask questions in natural language
• ask for page/slide references

For a more robust internal search:

• extract text
• chunk it
• embed it
• store it in a vector database
• query via RAG

7) Tips to get better results

• Use OCR for scans
• Keep slides as PPTX if possible, not flattened PDFs
• Chunk by section/slide/page, not arbitrary length when possible
• Ask specific questions
• Request citations
• Combine keyword + semantic search for best accuracy

If you want, I can also give you:

1. a no-code workflow,
2. a Python example, or
3. a recommended stack for searching a folder of PDFs and slide decks.