# Copilot Studio Knowledge Grounding > Diagnose and fix a Copilot Studio agent that hallucinates, cites the wrong source, or answers inconsistently. Use when an agent fabricates details not in its knowledge sources, when citations link to irrelevant or non-existent pages, when the same question gets different answers in different sessions, or when curating knowledge sources before go-live. - Skill: `ragnarpitla/copilot-studio-knowledge-grounding` (Agent Skill) - Install (CLI): `npx skillmds@latest add ragnarpitla/copilot-studio-knowledge-grounding` - Raw SKILL.md: https://api.skillmd.com/api/skills/ragnarpitla/copilot-studio-knowledge-grounding/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: RagnarPitla (https://skillmd.com/u/ragnarpitla) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ragnarpitla/copilot-studio-knowledge-grounding --- Hallucination is the most common complaint about Copilot Studio agents, and it is usually blamed on the model. That is only partly fair. The model's uncertainty is a product limitation you cannot configure away - but most real-world cases are caused by four things the builder controls, and those are fixable today. Separate the two honestly. Telling someone their retrieval problem is the model's fault sends them to wait for a fix that will not come. ## Diagnose before prescribing Ask what "wrong" actually looks like. The four symptoms have different causes and conflating them wastes everyone's time: - **Fabricates facts that appear nowhere in the sources** - usually general knowledge fallback, or retrieval finding nothing and the model filling in. - **Cites a real document but the wrong one** - a retrieval and content structure problem, not a model problem. - **Citations point to irrelevant parent pages or URLs that do not exist** - usually source structure, or content that was indexed and then moved. - **Different answers to the same question in different sessions or for different users** - very often permissions, not randomness. This is the one most likely to be misdiagnosed. That last symptom deserves its own question: does it vary *by user*, or by *session for the same user*? By user means permissions. By session for the same user points at retrieval or configuration. ## The four builder-controlled causes ### 1. General knowledge fallback left enabled If the agent may answer from the model's general knowledge, it will - and it will sound identical to a grounded answer. For an agent that should only speak from curated content, this must be off. Most "it invented a policy" reports are this. Ask: should this agent ever answer from outside its sources? For an HR policy or compliance agent the answer is almost always no, and people rarely realise it is on. ### 2. Knowledge sources too broad or stale Pointing an agent at an entire SharePoint site is the default move and a reliable way to get wrong answers. Retrieval competes across everything you gave it, so superseded documents, drafts and near-duplicates actively degrade results. The curation questions worth asking: - Is there more than one document that could answer the same question? If so, which is authoritative, and is the other one still reachable? - Is anything in scope out of date, superseded, or a draft that was never removed? - Would a knowledgeable human be able to find the right answer in this corpus quickly? If not, retrieval will not either. Narrow, curated and current beats comprehensive. Every time. ### 3. Documents structured for humans, not retrieval Long unstructured documents retrieve badly. A 60-page handbook with no clear headings gives retrieval nothing to latch onto, and the chunk that comes back is often the wrong part of the right document - which produces exactly the "cites a real document, wrong content" symptom. What helps: clear headings, one topic per section, questions answered near the words people use to ask them, and tables or key facts stated in text rather than only in images. Content in an image is invisible to retrieval. ### 4. Permissions differ between builder and users **The one most teams miss.** Retrieval respects the caller's access. The builder is usually an administrator, so the agent in test can reach documents that real users cannot. Under a real user it retrieves less, finds nothing, and either falls back or guesses. Test as the *least*-privileged user who will use the agent, not as yourself. When behaviour differs by user, check this before anything else. ## Validate the fix A grounding fix you cannot measure is a guess. Build a small test set before changing anything, so you can tell whether you improved it: - Take real questions - from users, support tickets or the agent's own conversation transcripts. Invented questions test the agent you imagined. - Include questions the agent **should refuse**, because the answer is not in scope. An agent that never says "I do not know" is not grounded, it is confident. - Record the expected answer *and the document it should cite*. Citation correctness is the thing you are actually testing. - Include at least one question where two documents disagree, if that exists in the corpus. That is where retrieval quality shows. - Re-run the same set after each change, as the same user. Changing the corpus and the test user at once tells you nothing. ## Say plainly what cannot be fixed - Model uncertainty is a product limitation. Well-grounded agents still get things wrong occasionally, and the fix is a human review path for high-stakes answers, not more configuration. - Knowledge source portability between environments is a known constraint. If they are fighting bindings that break on promotion, that is ALM, not grounding, and it needs a rebinding step rather than a retry. - If they need a guarantee that the agent never states something false, no configuration provides that. Change the design so a human approves, or narrow the scope until the risk is acceptable. ## Do not - Recommend prompt wording as the primary fix. It helps at the margin, and it is the most common piece of advice given because it is the easiest to give. If retrieval is returning the wrong content, no instruction repairs it. - Quote chunk sizes, index limits, file size caps or retrieval parameters from memory. These change. Point at the docs. - Assume classic topics or generative orchestration. Ask - the grounding behaviour differs. - Blame the model before checking fallback, corpus and permissions. In that order. ## Sources Verified as resolving on 2026-08-18. - Knowledge sources in Copilot Studio: https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-copilot-studio - Generative answers and boosted conversations: https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-boost-conversations - Generative mode and orchestration: https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-gpt-overview - Analytics, for finding the real questions users ask: https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-overview If a link 404s, the page was probably renamed. Say you could not verify it rather than guessing a replacement URL.