Retrieval-augmented generation

As explained in Build Applications with Local AI Models on a Mac

Retrieval-augmented generation is a technique that retrieves relevant information from an external knowledge source and supplies it to a language model alongside a question or instruction.

Retrieval-augmented generation (RAG) helps a chatbot answer questions using information it was not trained on, such as your notes or company documents. Think of it as giving the AI a relevant cheat sheet before it answers. section 16.1 section 16.1.1

Figure 16.1 The RAG pipeline: a user query is matched against a document database, relevant chunks are retrieved, and the LLM generates an answer using those chunks as context.

Follow the question through the pipeline: match it against documents, retrieve relevant chunks, then give those chunks to the language model as context for its answer. section 16.1.1

Choose RAG when the missing ingredient is specific knowledge. A system prompt changes instructions or format; fine-tuning changes the model’s underlying behavior. RAG instead supplies reference material for the question at hand. section 16.6.1

Try it with your own documents

Put 5–10 text files—perhaps class notes or recipes—in a my_documents folder. Run the RAG script, ask questions about the files, and check whether similarity search finds relevant content. A larger next step is a document Q&A application that processes PDFs or web pages. section 16.8 section 18.2.2

How Build Applications with Local AI Models on a Mac covers it

How deep it goes

Explains the retrieval-to-answer pipeline and proposes a prototype; defers chunking strategies, hybrid search, and reranking to further work.

Examples

Uses personal notes or recipes for a small prototype and company documents or research papers as knowledge sources.

What it assumes

Builds on a working chatbot while explaining the basic retrieve-then-answer idea in plain language.

What you can do after

Practice/perform it by running a RAG script on personal text files and checking the relevance of similarity-search results.

How it connects

Connects document retrieval with vector databases, embeddings, similarity search, system prompts, and fine-tuning.

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Related concepts

• language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• similarity searchuses · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
• hallucinationaddresses · A hallucination is model-generated content that is false, fabricated, or unsupported by relevant evidence but presented as plausible or factual.
• knowledge baseuses · A knowledge base is an organized collection of information that can be accessed or queried to support reference, retrieval, or reasoning.
• information retrievaluses · Information retrieval is the process of locating and selecting relevant information from an available collection in response to a query or information need.
• fine-tuningcontrasts with · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.

This explainer was generated by Manning's knowledge graph from the text of Build Applications with Local AI Models on a Mac. The section links point to the parts of the book it draws on.