RAG

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 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.

Workflow

Build an AI Docs Chatbot

A RAG-powered chatbot that answers questions from your documentation with citations — the kind every SaaS site needs in 2026.

Workflow

Build an AI Research Assistant

A research agent that takes a question, searches the web, reads sources, and produces a cited briefing.

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 Study

Case Study: RAG Docs Chatbot Shipped in 3 Weeks

How a 12-person B2B SaaS team shipped a cited docs chatbot that deflected 34% of support tickets — without hiring an ML team.

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.

Directory

Crawl4AI

Open-source web crawler built for LLM pipelines — async Playwright, clean markdown output, and LLM-friendly extraction.

Directory

Firecrawl

Web-to-markdown API for AI pipelines — crawl sites, extract clean content, and feed RAG indexes without brittle scrapers.

Directory

Haystack

deepset's open-source Python framework for building RAG pipelines and search systems from composable components.

Directory

LlamaIndex

Open-source framework for connecting LLMs to data with indexes and query engines; LlamaCloud adds managed parsing.

Directory

Ragie

Managed RAG-as-a-service API with connectors, chunking, hybrid search, and reranking for product integrations.

Directory

Vectara

Enterprise RAG platform-as-a-service with built-in retrieval, reranking, and hallucination detection.