rag

How Vector Search Actually Finds the Right Answer

A plain explanation of similarity search — how an AI chatbot finds the right chunk of your docs to answer from, and why it works by meaning, not keywords.

August 31, 20263 min read

embeddings

Chunking and Embeddings 101: How Your Docs Become Searchable

A plain explanation of chunking and embeddings — the two steps that turn a document into something an AI chatbot can search and answer from.

August 26, 20263 min read

ai chatbot

Grounded vs. Hallucinated Answers: How to Tell the Difference

What separates an AI answer that's actually grounded in your documentation from one that's confidently made up — and why the distinction matters for support.

August 24, 20263 min read

ai chatbot

Anatomy of One Chatbot Conversation

A single, realistic support conversation annotated turn by turn — what's actually happening behind each reply, from retrieval to escalation.

August 22, 20263 min read

ai chatbot

Why "Just Add ChatGPT" Doesn't Work for Customer Support

A general-purpose AI model dropped into a support widget doesn't know your product. Here's why grounding it in your own docs is the part that actually matters.

August 22, 20262 min read

rag

What Is RAG (Retrieval-Augmented Generation), Explained for Non-Engineers

A plain-language explanation of retrieval-augmented generation — the technique behind AI chatbots that answer from your own documentation instead of guessing.

August 20, 20263 min read

How Vector Search Actually Finds the Right Answer

Chunking and Embeddings 101: How Your Docs Become Searchable

Grounded vs. Hallucinated Answers: How to Tell the Difference

Anatomy of One Chatbot Conversation

Why "Just Add ChatGPT" Doesn't Work for Customer Support

What Is RAG (Retrieval-Augmented Generation), Explained for Non-Engineers

Ready to turn your docs into a chatbot?