Retrieval-Augmented Generation (RAG) with Embeddings & Vector Databases

Scrimba develops retrieval through embeddings and the vector databases that store and search them. The course connects those foundations with AI application examples. It suits JavaScript learners who want to understand how retrieval chooses context before using that context in generation, rather than assume a vector-search result is automatically the right evidence.

What you will learn

• Understand embeddings and similarity search.
• Explore vector-database workflows.
• Connect retrieved context with AI applications.

Who this suits

A solid understanding of basic JavaScript is required by the provider.

Before you enroll

Model APIs, vector databases and hosting may cost extra. Verify package versions before reproducing demonstrations. Use fictional or permissioned documents and test access controls. Evaluate retrieval quality separately from answer quality, include questions the documents cannot answer and check citations. A working demonstration does not establish factual accuracy, resistance to malicious documents or production reliability.

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Put it into practice

Compare search results for two similar questions and explain why a semantically close passage may still fail to answer the actual question.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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