Lesson 7.4: Where RAG shines, and where it fails | GeekHub Learn

Most failed RAG projects in 2026 used RAG for the wrong task. This lesson is the screening test.

A search engine plus a writer is great for "tell me what these docs say". Less great for "predict next quarter's revenue".

RAG shines when:

The answer is somewhere in your corpus, in roughly its current form
Questions are specific
Sources are text-heavy and well-structured
Updates are frequent
Answers must cite sources

RAG fails when:

The answer requires synthesis across hundreds of pages
Questions are open-ended ("what should we do?")
The corpus is mostly images or audio (use multimodal retrieval)
The data is fragmented (each chunk lacks self-contained context)
The user expects creativity, not retrieval

A failure pattern: chunks that lack headers or context. A chunk that says "It is 12.5%" with no context is useless. Always preserve metadata (document title, section, date) on each chunk so the retrieved snippet is self-contained.

Visualize it

A two-column table "RAG shines" vs "RAG fails" with 6 examples each.

Try it now

Pick 5 questions about your own notes. Identify which would and would not be answerable by RAG and why.

Hands-on lab

Take 2 documents you wrote. Identify 3 questions each. Predict RAG success. Once you build the RAG system in Module 9, come back and check.

Try it now

Why is "what should we do?" a bad RAG question?

Common mistakes

Using RAG for predictive or judgment questions
Letting chunks drop their source metadata
Hoping the LLM "synthesizes" facts that are not in any retrieved chunk

Debugging tip

If retrieval looks good but answers are wrong, your chunks are likely missing context. Add titles, dates, and section headers to every chunk.

Challenge

Audit a real "AI chatbot" you have used. Identify 3 questions it answered well and 3 it dodged or hallucinated. Diagnose which were RAG-mismatched.

Where this shows up

Good: customer support, internal docs, code search
Bad: open-ended brainstorming, forecasting, creative writing

From the field

Saying no to a wrong RAG use case earns more trust than building it badly. Build the muscle of screening.

Recap

RAG shines on specific, source-grounded questions. It fails on judgment, prediction, and synthesis past one doc. Screen ruthlessly.

Quick recall

3 prompts · think before you flip

Prompt 1 of 3

Name 3 RAG-suited tasks.

Quiz time

1 question · tap an answer to check it

1. RAG is a poor fit for