Quick answer

RAG (retrieval augmented generation) solves a specific problem plain language models have: they only know what was in their training data, which means they can’t answer questions about your private documents, or anything that happened after their training cutoff, without help. RAG fixes this by first searching a relevant set of documents for information related to the question, then feeding that retrieved information to the model alongside the question, so it can answer using real, current, specific information instead of guessing from memory. DeepLearning.AI offers several free short courses specifically on RAG, and Activeloop offers a free certification course on building RAG apps with LangChain and LlamaIndex.

RAG is one of the most practically useful techniques in applied generative AI work today, precisely because it solves such a common, concrete problem: making an LLM answer accurately about information it was never trained on, without the cost and complexity of fine-tuning.

The problem RAG actually solves

A language model’s knowledge is frozen at the point its training data was collected. Ask it about your company’s internal documentation, a recent event, or anything specific to your own data, and it will either say it doesn’t know, or worse, confidently make something up, a failure mode often called hallucination. RAG addresses this directly by giving the model real, relevant source material to work from at the moment it answers, rather than relying purely on what it memorized during training.

  • Without RAG: the model answers purely from what it learned during training, which may be outdated, incomplete, or simply doesn’t include your specific data at all.
  • With RAG: the model is given relevant, retrieved documents alongside the question, and answers based on that real material, with a much lower chance of inventing an answer.
RAG retrieves real, relevant information first, then hands it to the model, rather than relying only on what the model memorized during training.

How RAG actually works, in plain terms

A RAG system has two main parts working together. First, a retrieval step searches a knowledge base, your documents, converted into a searchable format, for content relevant to the question being asked. Second, a generation step feeds that retrieved content to the language model along with the original question, so the model’s answer is grounded in real, specific source material rather than pure memory.

  • Documents get converted into embeddings: a numerical representation that captures meaning, which lets the system find content that’s conceptually related to a question, not just keyword matches.
  • A vector database stores and searches these embeddings: quickly finding the most relevant chunks of your documents for a given question.
  • The retrieved content gets passed to the LLM: along with the original question, so the model generates its answer using that real material as context.
Retrieval and generation are two connected steps, not one single process, which is why RAG systems have more moving parts than plain prompting.

The best free courses to learn RAG

  • DeepLearning.AI’s short courses on RAG: several free, focused courses covering specific parts of building RAG systems, including work with knowledge graphs alongside standard retrieval.
  • Activeloop’s free RAG certification course: covers building RAG applications specifically with LangChain and LlamaIndex, two of the most widely used tools for this.
  • Boot.dev’s interactive RAG course: free to start, with hands-on, code-first lessons rather than pure video content.
These free courses cover RAG from slightly different angles, conceptual short courses versus hands-on, tool-specific practice.
Grounding answers in retrieved, real documents is what measurably reduces hallucinated responses.

Where RAG fits alongside fine-tuning and prompting

RAG, fine-tuning, and better prompting solve different problems, and they’re often used together rather than as competing choices. If the issue is that a model doesn’t have access to specific or current information, RAG is usually the right tool. If the issue is inconsistent style, tone, or format, fine-tuning tends to be more effective; see our guide to free LLM fine-tuning courses for that comparison in more depth. If you’re building a real application around either of these, our guide to free LLM application development courses covers the broader engineering picture.

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Frequently asked questions

What does RAG stand for and what does it actually do?

RAG stands for retrieval augmented generation. It retrieves relevant information from a knowledge base, like your own documents, and feeds that information to a language model alongside a question, so the model answers using real, specific, current material instead of relying only on what it memorized during training.

Why can’t a language model just answer questions about my own documents directly?

A language model’s knowledge is fixed at the point its training data was collected, so it has no access to your private documents or anything that happened after training, unless that information is explicitly provided to it. RAG solves this by retrieving and providing that information at the moment the question is asked.

What’s the best free course to learn RAG?

DeepLearning.AI offers several free short courses specifically on RAG, and Activeloop offers a free certification course covering RAG application development with LangChain and LlamaIndex, two widely used tools for building these systems.

What’s the difference between RAG and fine-tuning?

RAG gives a model access to specific, current, or private information it wasn’t trained on, by retrieving it at the moment of answering. Fine-tuning changes how a model behaves, its style, tone, or consistency, by continuing its training on a focused dataset. They solve different problems and are sometimes combined.

Do I need to know how vector databases work to learn RAG?

You’ll need at least a working understanding, since vector databases are what store and search the retrievable content in most real RAG systems. Most free RAG courses introduce this concept as part of the course rather than assuming you already know it.