Definition
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) grounds a language model's answers in your own data by retrieving the most relevant documents at query time and feeding them into the model's context before it responds. Instead of relying solely on what the model memorized in training, RAG lets it answer from current, authoritative, private sources — sharply reducing hallucination.
Key takeaways
- RAG grounds a model's answers in your own data by retrieving the most relevant documents at query time and feeding them into context before it responds.
- It lets the model answer from current, authoritative, private sources instead of training memory alone — sharply cutting hallucination.
- Answers can cite their retrieved sources, stay fresh as data changes, and keep private data scoped to a single request (no retraining).
- RAG lives or dies on retrieval quality — surface the wrong passages and the model answers confidently from bad evidence.
A bare language model can only draw on what it absorbed during training: it has no knowledge of your private data, and what it does know may be stale. RAG closes both gaps. When a question comes in, the system first searches a knowledge base — usually with semantic search over vector embeddings — for the passages most relevant to the query, then inserts those passages into the model's context alongside the question. The model answers from that supplied evidence rather than from memory alone.
The payoff is accuracy and freshness with attribution. Because the answer is built from specific retrieved sources, it can cite them, be checked against them, and stay current as the underlying data changes — no retraining required. It also keeps private data private to the request: the documents inform a single answer rather than being baked into the model. This is why RAG became the default pattern for AI that must answer over an organization's own, ever-changing information.
RAG lives or dies on retrieval quality. If the search step surfaces the wrong passages, the model answers confidently from bad evidence — garbage in, fluent garbage out. Good RAG therefore invests heavily in the retrieval layer: clean chunking, strong embeddings, relevance ranking, and enough but not too much context, all bounded by the model's context window.
Planoda indexes workspace issues and documents as vector embeddings, so its AI features retrieve the relevant, tenant-scoped context and answer from your actual data — grounded, current, and isolated to your workspace.
Related terms
- Semantic SearchSemantic search finds results by meaning rather than exact keywords, using vector embeddings that place similar concepts near each other in mathematical space. A query for 'login broken' can surface an issue titled 'users can't authenticate' even with no shared words. It powers more relevant search and is the retrieval layer behind many AI features.
- Large Language Model (LLM)A large language model is an AI system trained on vast amounts of text to predict and generate language, enabling it to answer questions, summarize, write, and reason over natural language. LLMs power modern AI assistants and agents. They are probabilistic next-token predictors, which makes them remarkably capable but also prone to confident errors.
- Context WindowA context window is the maximum amount of text — measured in tokens — that a language model can consider at once, spanning its input and its output. Everything the model 'sees' for a given request must fit inside it: the prompt, retrieved data, conversation history, and the answer. When content exceeds the window, the oldest or least relevant parts must be dropped.
- AI AgentAn AI agent is a software system that uses a large language model to pursue a goal across multiple steps — reading context, choosing tools, and taking actions — rather than answering a single prompt. In a work platform, agents triage issues, draft updates, and execute multi-step tasks as autonomous teammates, bounded by the permissions and approvals their operators set.