Talk to Your Documents

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  • 13 Jun, 2025

Talk to Your Documents: A Practical Dive into Retrieval-Augmented Generation (RAG)

Most people don’t talk to documents—they search them. But that’s changing fast.

Retrieval-Augmented Generation (RAG) flips the script. Instead of just feeding a language model a question and hoping it knows the answer, RAG lets you ask questions with the help of your own content. Whether it’s internal documentation, research papers, or customer transcripts, your documents become a searchable memory for AI.

I recently built a toolchain to demonstrate how this works using WWDC transcripts from Apple’s developer conference. It’s a practical example of how to prepare content for local RAG applications—and along the way, it taught me a lot about what actually matters when you’re getting AI to “talk” to your data.

Why Metadata Matters More Than You Think

A lot of people throw raw documents into a vector database and call it a day. But context matters.

If you want accurate, relevant responses, you need to preserve the metadata:

  • Source – Where did the information come from? (e.g., WWDC2025)
  • Date – When was it said or written?
  • Structure – What’s the main topic? What subtopic does this paragraph fall under?

Without that, you’re just hoping the model will figure it out. (Spoiler: it won’t.)

My Pipeline: From Transcripts to RAG-Ready Chunks

The project has two main parts:

  1. Transcript Extractor A Python script that scrapes WWDC session transcripts directly from Apple’s site and saves them as markdown files, complete with metadata like title, year, and source URL.

  2. LLM-Assisted Chunker A second script runs those transcripts through a local language model via LM Studio, splitting the content into semantically meaningful chunks. Each chunk gets a title, a summary, and keeps the original metadata so retrieval stays precise.

All of this runs locally—no API calls, no vendor lock-in, and full control over how your documents are prepared.

Why I Built It This Way

Most RAG tutorials stop at “put it in a vector store.” I wanted to understand what comes before that step—how to make your documents intelligent enough that AI can use them effectively.

So this isn’t just about getting something working. It’s about getting it right:

  • Chunking that respects topic boundaries
  • Metadata that survives the preprocessing step
  • Support for open-source models, not just OpenAI or Anthropic

The Bigger Picture

If you’re exploring how to integrate AI into your business—especially if you’re dealing with a large corpus of internal knowledge—you need more than just a chatbot interface. You need infrastructure that turns your data into something a model can use effectively.

That’s what this project represents: a small but real step toward that kind of capability.

Interested in applying this to your business?
I work with teams to implement practical, no-nonsense AI solutions—from RAG workflows to intelligent internal tools.

Get in touch to start a conversation.

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