RAG &amp; Knowledge Systems

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.

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

Build a document-grounded assistant and know when stale, low-quality, or out-of-scope sources make answers unsafe.

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.

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

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

10 min read

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.

Company knowledge RAG: permissions, leakage, and source boundaries

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

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.

Unlock Better RAG & AI Agents with Docling

20 minutes

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.

Permissions & Access Control for RAG - a Deep Dive Tutorial

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

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

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

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.

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

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

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.

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

Use the article as decision context for adoption, risk, governance, or investment choices.

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.

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

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

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.

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

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

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.

The AI customer support agent that resolves 70% of tickets

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

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.

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

Design a lead-triage agent with explicit tools, schemas, routing rules, logging, and human review.

9 minutes

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.

RAG vs. Fine Tuning

13 minutes

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.

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

16 minutes

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.

Graph RAG: Improving RAG with Knowledge Graphs

39 minutes

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.

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

17 minutes

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.

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

19 minutes

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.

Building Production-Ready RAG Applications: Jerry Liu

69 minutes

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.

The 5 Levels Of Text Splitting For Retrieval

24 minutes

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.

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

11 minutes

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.

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

26 minutes

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.

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