Submits regulatory query
Remember RAG
Full name: Retrieval-Augmented Generation
Memory aid: Retrieve first, Augment context, Generate an answer.
Retrieval can improve grounding but does not guarantee a correct answer.
Study area: AI & machine learning · Batch 4
Relevant study for: Azure AI Fundamentals AI-901 · Azure AI Apps and Agents Developer AI-103 · Azure AI Fundamentals (retired) AI-900 · Azure AI Engineer (retired) AI-102
Practice with exam flashcards →AI Models & Assistants / Knowledge library
Retrieval-Augmented Generation
rag retrieve evidence before generating answers
Why learn Retrieval-Augmented Generation?
Helps ground answers in supplied information without assuming it is memorized by a model. Retrieval does not guarantee truth, source quality, or authorization.
Where it came from
Retrieval-augmented generation combines retrieval from an external collection with model-generated output. The original RAG paper studied a model combining parametric and non-parametric memory.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Build a corpus with known answer locations.
- Retrieve passages before generating an answer.
- Attach source IDs to supported claims.
- Test questions whose answers are absent from the corpus.
- Measure retrieval recall and answer faithfulness separately.
Documentation and sources
- Original RAG research paper (arxiv.org)
Catalog research notes
- Research snapshot: September 9, 2026. This entry summarizes the linked primary documentation; it does not assert a latest release version.
Connect the ideas
Built on
Enables
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Real World Example
Maritime Regulatory Compliance Assistant
Global shipping logistics firms that must adhere to rapidly changing international maritime safety and environmental regulations.
The business problem
Large language models often hallucinate outdated safety protocols because their internal parametric memory is static and lacks access to the latest, frequently updated regulatory PDF documents.
How this topic solves it
Retrieval-Augmented Generation (RAG) addresses this by decoupling factual knowledge from the model's parameters. Instead of relying on training data alone, the system queries a dynamic, non-parametric vector index containing the most recent regulatory filings before generating a response, ensuring the output is grounded in current, verifiable evidence.
- Ingest current maritime regulatory documents into a searchable vector database.
- Implement a neural retriever to perform semantic similarity searches against the vector index based on user queries.
- Pass the retrieved, relevant document chunks as context to a pre-trained seq2seq model.
- Generate a final response that synthesizes the retrieved evidence with the model's linguistic capabilities.
This diagram illustrates the flow from a user query to the final grounded response.
Searches vector index
Stores regulatory documents
Generates grounded response
- Compliance Officer → query → Neural Retriever
- Neural Retriever → semantic search → Vector Database
- Vector Database → retrieved evidence → Seq2Seq Model
- Seq2Seq Model → grounded answer → Compliance Officer
Expected business outcome
The system provides responses that are grounded in specific, cited regulatory documents, significantly reducing the risk of providing obsolete or incorrect safety guidance.
Limitations and checks
A primary limitation is the dependency on the quality of the retriever; if the retrieval step fails to surface the most relevant document chunks, the model may generate an answer based on incomplete context. This can be checked by monitoring the semantic relevance scores of the retrieved passages against the user query.