RAG &amp; Knowledge Systems

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

Build your own document-grounded chat in under an hour, with no code. The three no-code options worth using in 2026, the tradeoffs, and the patterns that distinguish a useful RAG from a frustrating one.

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.

10 min read

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.

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9 min read

A personal knowledge system that helps you retrieve, not hoard

Highlights, clippings, and AI summaries pile up without making you smarter. A small capture-to-retrieval system built around three real decisions — plus what to do about sensitive notes, other people's data, and the false privacy of a personal account.

20 minutes

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.

7 minutes

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.

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.

The AI customer support agent that resolves 70% of tickets

A realistic design for an AI customer support agent that resolves the common cases, escalates the hard ones, and doesn't make the kind of mistake that ends up on Hacker News. The architecture, the prompts, the guardrails.

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.

Context engineering: managing 1M-token windows without context rot

1M-token context windows exist, but quality degrades long before that limit. Context engineering is the discipline of using context windows effectively — what to include, what to summarize, what to retrieve fresh, and the patterns that keep quality high as context grows.

Build your first AI agent in n8n: a lead-triage workflow end-to-end

A complete walk-through of building a real AI agent in n8n — one that triages incoming leads, enriches them, scores them, and routes them. Every node, every prompt, every gotcha.

Choosing between prompting, RAG, and fine-tuning (and when to combine)

Prompting, RAG, and fine-tuning are the three big levers for adapting LLMs to your problem. Each is right for some problems and wrong for others. A framework for choosing, the realistic costs of each, and the production patterns where combining them shines.

RAG beyond chunks: graph RAG, agentic RAG, long-context RAG

Classic chunk-based RAG has limits. Graph RAG, agentic RAG, and long-context RAG each break those limits in different ways. When each is the right tool, how they actually work, and the production trade-offs that matter.

11 minutes

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.

26 minutes

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.

69 minutes

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.

24 minutes

"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.

17 minutes

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.

19 minutes

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.

9 minutes

RAG vs. Fine Tuning

IBM Technology. Tighter focus on the two techniques teams most often confuse. Goes deeper on data freshness, source attribution, and the inference-time speed argument for fine-tuning. Worth watching if you are specifically trying to argue against an unnecessary fine-tune project.

13 minutes

RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models

IBM Technology. A clear whiteboard pass through all three techniques with their respective costs — retrieval latency, training compute and catastrophic forgetting, the limits of prompt-only solutions — and the combinations that actually make sense in production. The closing example of a legal AI system using all three is almost exactly the article's "when to combine" argument.

39 minutes

Introducing RAG 2.0: Agentic RAG + Knowledge Graphs (FREE Template)

Cole Medin. A working agentic-RAG-plus-knowledge-graph build, with the agent deciding when to do vector search, when to hit Neo4j, and when to do both. It's the cleanest demonstration on YouTube of the "agent as the retrieval planner" pattern the article describes, in code you can actually pull down and run.

16 minutes

Graph RAG: Improving RAG with Knowledge Graphs

Prompt Engineering. A focused walkthrough of Microsoft's GraphRAG — entity extraction, community summaries, query-focused summarization — set up on a local machine with cost notes. Watch it for the graph-RAG section of the article specifically; the cost discussion is the part most write-ups skip.