Documents & PDFs

7 minutes

Video

Unlock Better RAG & AI Agents with Docling

IBM Technology. Explains the ingestion side of RAG and agents: preparing PDFs and other files so document structure, tables and layout survive into downstream retrieval. That supports the article's warning that RAG quality and safety begin before embedding, especially when parsing complex business documents.

Advanced

20 minutes

Video

Permissions & Access Control for RAG - a Deep Dive Tutorial

Paragon. Walks through the production RAG permission problem and compares tool-calling, namespaces, ACL tables and relationship-based permissions. That directly supports the article's core rule: retrieval must only return sources the current user is allowed to see, and source-system permissions cannot be treated as an afterthought.

Advanced

11 min read

Article

Secure document ingestion for RAG: PDFs, OCR, metadata, and retention

RAG quality starts before retrieval. A secure ingestion guide for PDFs, OCR, metadata, permissions, source freshness, deletion, malware risk, and operational ownership.

Design a secure document-ingestion pipeline for RAG with permission metadata, OCR quality checks, source freshness, retention rules, deletion behavior, and ingestion tests.

Advanced

10 min read

Article

Company knowledge RAG: permissions, leakage, and source boundaries

A company knowledge assistant is only safe if retrieval respects permissions. How to design RAG source boundaries, ACL filtering, document ownership, logging, stale-source handling, and refusal behavior.

Design a company knowledge RAG with permission-aware retrieval, source ownership, leakage controls, and refusal behavior.

Advanced

12 min read

Article

Building a production RAG: ingestion, embedding, retrieval, reranking, eval

A production RAG pipeline is six stages, each with specific patterns that determine quality. The architecture, the choices at each stage, and the iterative evaluation discipline that distinguishes RAG that works from RAG that disappoints.

Evaluate the implementation pattern, failure modes, and guardrails before building.

Advanced

11 min read

Article

Chunking, reranking, and hybrid search: make RAG actually work

Most RAG implementations work poorly because they get three things wrong. A practical guide to chunking documents, reranking results, and combining keyword with semantic search — without becoming a search engineer.

Evaluate the implementation pattern, failure modes, and guardrails before building.

Intermediate

17 minutes

Video

RAG Agents in Prod: 10 Lessons We Learned — Douwe Kiela, creator of RAG

AI Engineer. Douwe Kiela led the original RAG paper at FAIR and now ships RAG into regulated enterprises. The talk is mostly about what stops working at scale — chunking strategies that don't survive 100k documents, "accuracy is table stakes, inaccuracy is the real problem," and why attribution and observability matter more than the embedding model. Good calibration before re-reading the article's eval and monitoring sections.

Advanced

19 minutes

Video

Building Production-Ready RAG Applications: Jerry Liu

AI Engineer. LlamaIndex's CEO walking the gap between "naive RAG demo" and a real pipeline — small-to-big retrieval, sub-question routing, hybrid search, evaluation. The shape of his slides maps almost directly onto the article's pipeline sections; watch first, then re-read the article with his diagrams in your head.

Advanced

69 minutes

Video

The 5 Levels Of Text Splitting For Retrieval

Greg Kamradt. The article spends a lot of words on chunking; this is the longest, most patient explanation of what each chunking strategy is actually doing — from character-recursive through document-aware to semantic and agentic splitting. Pair it with Greg's free ChunkViz tool to build intuition before you start tuning.

Intermediate

24 minutes

Video

"I want Llama3 to perform 10x with my private knowledge" - Local Agentic RAG w/ llama3

AI Jason. Covers the exact stack the article argues for — query translation, hybrid retrieval, reranking, and a corrective-RAG loop — in one runnable build. Useful as a working mental model for what the chunk → rerank → answer pipeline looks like when it's actually doing its job.

Intermediate

11 minutes

Video

How To Use NotebookLM For Beginners In 2024 (NotebookLM Tutorial)

TheAIGRID. A faster, feature-first tour: uploading mixed sources (PDFs, YouTube transcripts, blog posts), generating a briefing doc, focusing the chat on a single source, and the audio-overview podcast. Good if you want a quick map of the surface area before committing time to a longer walkthrough.

Intermediate

26 minutes

Video

How to Use NotebookLM (Google's AI "Tool for Understanding")

Tiago Forte. Tiago is the *Building a Second Brain* author and treats NotebookLM as exactly what the article describes — a personal RAG over your own notes, PDFs and clippings. He shows the citation-grounded chat, the limits of the tool, and how it fits next to a Readwise/Obsidian workflow, which is the natural endpoint for most readers of the article.

Intermediate

28 minutes

Video

35+ INSANE Ways To Use NotebookLM (For FREE)

Matt Wolfe. Once you understand the basics, this is the right second video. Matt runs through a long list of less obvious uses — turning a book into a study guide, briefing yourself on a competitor from their content, generating mind maps from a folder of PDFs — that help you see how flexible the "grounded notebook" frame actually is.

Beginner

35 minutes

Video

How To Master NotebookLM in 2026 (Free Course)

Paul J Lipsky. A clean three-step framing — curate sources, ask the right questions, produce final outputs — that maps almost one-to-one onto how the article tells you to think about NotebookLM. Recent enough (early 2026) to match the current UI, including Studio, audio overviews, and mind maps.

Beginner

18 minutes

Video

This NotebookLM + Perplexity Workflow Will Cut Your Research Time by 50% (or More)

Grace Leung. A more practical two-tool workflow: Perplexity for finding the documents and citations, NotebookLM for actually reading and synthesising them. Useful if your "long document" problem is really a "long stack of documents" problem — market research, regulatory filings, multi-source reports.

New to AI

26 minutes

Video

How to Use NotebookLM (Google's AI "Tool for Understanding")

Tiago Forte. NotebookLM is the AI tool the article most enthusiastically recommends for long documents, and Tiago Forte — the Building a Second Brain guy — gives the cleanest tour of why. He demonstrates summarising meeting notes, querying long PDFs, and the source-grounding feature that stops the model from inventing facts the documents don't contain. After watching, you'll understand why "upload the PDF to NotebookLM" is the article's default suggestion.

New to AI

Start here

Using AI to read long documents, contracts, and PDFs

NotebookLM: turn any source into a personal knowledge base

Build a personal RAG: chat with your own documents (no code)

More in this topic

Unlock Better RAG & AI Agents with Docling

Permissions & Access Control for RAG - a Deep Dive Tutorial

Secure document ingestion for RAG: PDFs, OCR, metadata, and retention

Company knowledge RAG: permissions, leakage, and source boundaries

Building a production RAG: ingestion, embedding, retrieval, reranking, eval

Chunking, reranking, and hybrid search: make RAG actually work

RAG Agents in Prod: 10 Lessons We Learned — Douwe Kiela, creator of RAG

Building Production-Ready RAG Applications: Jerry Liu

The 5 Levels Of Text Splitting For Retrieval

"I want Llama3 to perform 10x with my private knowledge" - Local Agentic RAG w/ llama3

How To Use NotebookLM For Beginners In 2024 (NotebookLM Tutorial)

How to Use NotebookLM (Google's AI "Tool for Understanding")

35+ INSANE Ways To Use NotebookLM (For FREE)

How To Master NotebookLM in 2026 (Free Course)

This NotebookLM + Perplexity Workflow Will Cut Your Research Time by 50% (or More)

How to Use NotebookLM (Google's AI "Tool for Understanding")