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Learning Objectives & Key Takeaways
0 / 4 Completed- Understand RAG core architecture and runtime execution model
- Audit real-world latency, throughput benchmarks, and memory footprint
- Implement zero-downtime deployment patterns with resilience guards
- Verify code snippets and architect blueprints against production SLOs
Practise it next
RAG interview questions, quiz and handbook in your Learning Dashboard
What you will learn
- Answer quality is decided at retrieval time, not by the prompt.
- Chunk by meaning (headings, paragraphs), not by fixed character counts.
- Measure retrieval and answers separately with a small labelled set.
The demo works, production does not
Retrieval-augmented generation looks simple: embed your documents, find the closest chunks to a question, paste them into the prompt. The demo answers ten questions perfectly. Then real users ask the eleventh, and the model confidently answers from the wrong page.
Almost every RAG failure is a retrieval failure. If the right passage never reaches the model, no prompt can rescue the answer.
In the rest of this story
- Chunking and metadata
- Hybrid retrieval with a re-rank
- Evaluate before you tune
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