Definition
Grounding
Grounding is the practice of tying an AI model's responses to verifiable, external source data rather than relying solely on what it absorbed during training. By supplying relevant, current, authoritative context at query time — and ideally citing it — grounding reduces hallucination and keeps answers accurate, traceable, and specific to the user's actual data.
Key takeaways
- Grounding ties a model's responses to verifiable external source data instead of relying solely on what it learned during training.
- It supplies relevant, current context at query time — most often via retrieval-augmented generation — and ideally cites the sources used.
- The payoff is accuracy, currency, and traceability: answers reflect your actual up-to-date data and can be checked against citations.
- Grounding sharply reduces hallucination but doesn't eliminate it, so pair it with instructions to defer when context is insufficient.
A model trained on a fixed corpus has a fixed, sometimes outdated, and entirely generic store of knowledge. Grounding overrides that by feeding the model real source material at the moment it answers — documents, records, or search results relevant to the question — and instructing it to base its response on that material. The model becomes a reasoner over supplied facts rather than a recaller of training-time impressions.
Retrieval-augmented generation is the most common grounding mechanism: semantically retrieve the most relevant content, place it in the prompt as context, and have the model answer from it. The payoff is accuracy and currency — the answer reflects your actual, up-to-date data — plus traceability, since grounded responses can cite the specific sources they drew on.
Grounding sharply reduces hallucination but does not eliminate it. A model can still misread, over-generalize, or stray from the supplied context, which is why grounded systems pair retrieval with instructions to defer when the context is insufficient, and with citations a human can check. The quality of the answer is bounded by the quality of what was retrieved.
Planoda grounds its AI in tenant-scoped workspace data retrieved through vector embeddings, so answers reflect a team's real, current information inside its own security boundary.
Related terms
- 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.
- Hallucination (AI)An AI hallucination is output from a language model that is fluent and confident but factually wrong, fabricated, or unsupported by the model's inputs — invented citations, fake quotes, or made-up details. It arises because models predict plausible text rather than retrieve verified facts, making hallucination an inherent risk that must be mitigated, not a bug that's simply fixed.
- EmbeddingAn embedding is a numeric vector that represents a piece of content — text, image, or other data — as a point in high-dimensional space, positioned so that similar items land near each other. Produced by a model trained to capture meaning, embeddings let software compare content by semantic similarity rather than by matching exact words.
- 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.