Topic · RAG
RAG
24 entries on AIKnowHub tagged "RAG", spanning 7 content types.
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Concepts8
Concept
Agent Memory Explained
How AI agents remember context across sessions — short-term window, long-term stores, vector memory, and when you do not need memory at all.
Concept
AI Hallucinations Explained
Why LLMs make things up confidently — what causes hallucinations, why they're hard to fix, and the practical ways to reduce them.
Concept
RAG Stack Under $100/mo
A production-capable retrieval stack for startups and side projects — ingestion, embeddings, vector store, LLM, and observability without enterprise bills.
Concept
Chunking Strategies for RAG
Chunking is the highest-leverage decision in any RAG system. Get it wrong and no embedding model or vector DB will save you. Here's how to chunk by document type.
Concept
RAG Explained
Retrieval-Augmented Generation is how you give LLMs access to your own data without retraining them. Here's how it actually works.
Concept
Reranking & Hybrid Search
Embeddings alone miss exact terms. BM25 alone misses meaning. Hybrid search combines both, then a reranker picks the best. Here's how to build the retrieval stack that actually works.
Concept
Top GitHub Repos Every AI Builder Should Know (2026)
Fifteen open-source projects that cover 90% of what AI engineers actually install — agents, RAG, observability, routing, and local inference.
Concept
Vector Databases Explained
What vector databases actually do, when you need one, and what the real tradeoffs between Pinecone, Weaviate, Qdrant, and pgvector look like.
Tool Guides2
Tool Guide
Dify Guide
The definitive guide to Dify for low-code AI apps — RAG chatbots, agent workflows, MCP import, self-host vs cloud, and when to pick it over Flowise or n8n.
Tool Guide
Flowise Guide
The definitive guide to Flowise for visual RAG and agent prototyping — drag-and-drop chains, 2.0 templates, MCP nodes, and when to pick it over Dify or n8n.
Workflows2
Comparisons4
Comparison
Best Embedding Model
OpenAI vs Voyage vs Cohere vs open-source — which embedding model wins on quality, cost, and ecosystem in 2026?
Comparison
Dify vs Flowise vs n8n for AI
Three ways to build AI apps without writing a full backend — low-code platform, visual chain builder, or automation canvas. When to pick each.
Comparison
Langfuse vs Braintrust vs Phoenix
Three observability options for production LLM apps — open-source tracing, eval-first SaaS, and RAG-focused OSS from Arize.
Comparison
RAG vs Fine-Tuning vs Long Context
Three ways to make an LLM work with your data. When to retrieve, when to train, when to paste — and when to use none of them.
Case Studys1
Interviews5
Interview
How do you handle PII in a RAG pipeline without breaking retrieval quality?
Tests privacy-aware AI design — a gap between demo RAG and enterprise deployment.
Interview
Walk me through designing a production RAG system for a docs chatbot.
System-design-style question that's now standard in AI engineering interviews.
Interview
When would you use RAG vs fine-tuning to give an LLM access to new information?
Tests practical LLM judgment — this is the #1 question in applied AI interviews in 2026.
Interview
Design a customer support LLM chatbot for a SaaS company. What's your architecture?
Now the most-common AI engineering interview question — RAG architecture under production constraints.
Interview
Design a multi-tenant RAG platform where each customer uploads private documents.
Tests isolation, scale, and SaaS thinking — the shape of most enterprise AI products.