RAG project settings explained

📋 Reference

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WHAT IT DOES

Every setting in a RAG project — what it does and when to change it — grouped by the five tabs you see when you create or edit one: General, Retrieval, Generation, Groundedness, and Experience. Sensible defaults are set for you; most projects only need a few of these touched. Tune based on what the RAG Projects test page shows.

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General

What the project is and what it answers with.

Setting	What it does / when to change
Name, Description	Label the project so collaborators know its purpose.
Knowledge base	The library this project searches. Required — without one, runs fail. See Knowledge Bases.
AI model	Which model writes the answer. Leave on Workspace default unless you need a specific model.
Persona / system prompt	Sets tone and role (e.g. "a concise, friendly city-services assistant"). Optional.

Retrieval — finding the right content

How the project searches your knowledge base for each question.

Query type — Simple (keywords), Semantic (meaning & context), Vector (embedding similarity), or the combined Vector + Simple / Vector + Semantic. Vector modes need a knowledge base with vector search turned on — if it isn't, the project stays selectable but quietly searches text only, so check the knowledge base first. Change it if the project misses obviously-relevant content — a vector or semantic mode handles natural-language questions far better than keywords. How the content was split doesn't restrict this choice; see Choosing a split style.
Search filter mode — Disabled, Hybrid, or Strict. Controls how custom-field filters narrow results. Leave Disabled unless you filter on custom fields.
Retrieved documents (Top-N) — how many passages the model sees per question (default 5, up to 50). More gives the model additional context but can add noise; raise it if answers miss detail, lower it if they wander.
Extract relevant sentences — passes only the relevant lines from each passage instead of the whole thing. Helps when passages are long.
Query optimization steps — pre-search steps that sharpen the question: Classify intent, Generate direct answer, Analyze specificity & ambiguity (asks a clarifying question when a query is vague — multi-turn, so it's skipped in the test page), and Refine query & extract entities. Turn off any you don't need.
Clean search results — strips unrelated text from results before the answer is written. Useful when passages are noisy.

Generation — writing the answer

How the model composes the answer once it has the content. The defaults produce focused, factual answers.

Setting	Default	What it does
Temperature	0.3	0 = deterministic, 1 = balanced, 2 = creative. Keep low for factual answers.
Top P	1	Nucleus sampling (0–1). Leave at 1 unless you know you need it.
Max response tokens	4096	Caps answer length. Raise for long-form answers.
Frequency penalty	0	Higher discourages repeating the same words.
Presence penalty	0	Higher encourages introducing new topics.

Groundedness — staying accurate

How tightly the answer must stick to your content — the most important group for trustworthy answers.

Limit responses to knowledge base content — on by default. The agent answers only from your material and says so when something isn't covered, instead of guessing. Set the out-of-scope reply it uses.
Groundedness strictness — Low (minor inferences allowed), Medium (close match, slight rewording), or High (must adhere to sources). Keep High for public-facing, factual agents.