RAG Explained for Non Engineers
Understand how AI assistants answer from company documents, what can go wrong and what to ask your team.
A taste of a lesson
A vendor demo answered every question perfectly. Is that enough to buy?
A demo shows the system at its best on questions someone chose. Ask to run it on your own documents with your own questions: collect fifty real questions from staff, including a few the documents cannot answer, and have experts check each answer and its sources. Also ask how permissions are enforced, how quickly document changes appear, and what the assistant says when it does not know. Which documents would you test it on first?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain retrieval augmented generation in plain terms
- Identify what drives answer quality, especially document quality
- Recognise questions RAG handles badly and alternatives for them
- Ask informed questions about permissions, freshness and costs
- Judge a system with your own set of real questions
Lesson plan
- 1 How RAG works Describe the search then answer process without technical terms. Start
- 2 What drives quality Understand why documents and search matter as much as the model. Start
- 3 Where RAG struggles Spot questions that need different tools. Start
- 4 Permissions, freshness and cost Ask the right operational questions before buying or building. Start
- 5 Judging a system Evaluate an assistant with your own questions and experts. Start
Try asking
About this tutor
For managers, product owners, analysts, knowledge managers and anyone asked to approve, buy or plan an AI assistant that answers from internal documents. Without code, you learn what retrieval augmented generation is, why it is used instead of retraining a model, what determines answer quality (mostly the documents and the retrieval step), and which questions it handles badly, such as counting across all documents. You also cover permissions, freshness, citations, costs and how to judge a system with a set of real questions. Each lesson ends with questions to ask your team or a vendor.
Reviews
4.7
3 ratingsSample
- Margaret O.Sample
As a knowledge manager I finally understand why our content quality matters so much for the AI project. The vendor questions list was gold.
- Ricardo P.Sample
Free, clear and no jargon. The point that RAG cannot count across all documents saved us from a bad use case.
- Yasmin H.Sample
Helpful for a non technical director. I would have liked a sample evaluation sheet, but the method was clear enough to build one.
About the teacher
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principles
9 tutors 374 lessons taught Sample
I come from search: indexes, ranking and the long tail of queries that make a search box look foolish. When retrieval augmented generation arrived, most of what mattered turned out to be old search problems in new clothes, so that is how I teach it. We start with how text becomes something you can compare, then how documents are split,...
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