RAG &amp; Knowledge Systems

Build a personal RAG: chat with your own documents

Build and evaluate a bounded document-grounded assistant. Compare hosted notebooks, project workspaces, and configurable retrieval without weakening provenance or permissions.

Production RAG design: ingestion, retrieval, reranking, and evaluation

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

A bounded capture-to-retrieval system with explicit use cases, retrieval tests, review dates, deletion rules, and warnings about sensitive notes, other people's data, and provider-held accounts.

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.

Design an AI customer support agent: triage, knowledge, actions, and escalation

A queue-evaluated reference design for support triage, retrieval, response drafting, controlled actions, and human escalation, with the policy and measurement boundaries needed for a safe pilot.

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: testing long-context systems without guesswork

Large context windows are capacity limits, not quality guarantees. Build position, distractor, retrieval, latency, and cost tests for the workload you actually run.

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

A documented n8n lead-triage design: validate input, constrain agent tools, validate structured output, persist an atomic proposal, and keep every customer-visible action behind approval.

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. This older feature-first tour shows mixed-source uploads, single-source focus, briefing documents, and Audio Overviews in the interface available when it was recorded. It is useful for recognizing workflow components, not for confirming the current Gemini Notebook interface or feature availability.

26 minutes

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

Tiago Forte. Tiago Forte, author of _Building a Second Brain_, demonstrates the product then called NotebookLM with notes, PDFs, and clippings. The useful pattern is selecting a bounded source set, asking document questions, inspecting linked passages, and deciding how the resulting artefacts fit into an existing knowledge workflow.

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.