diff --git a/admin/app/controllers/ollama_controller.ts b/admin/app/controllers/ollama_controller.ts
index 3bf9e73..9174616 100644
--- a/admin/app/controllers/ollama_controller.ts
+++ b/admin/app/controllers/ollama_controller.ts
@@ -106,8 +106,17 @@ export default class OllamaController {
             `[RAG] Injecting ${trimmedDocs.length}/${relevantDocs.length} results (model: ${reqData.model}, maxResults: ${maxResults}, maxTokens: ${maxTokens || 'unlimited'})`
           )
 
+          // Label each context block with its source title when available (a neutral,
+          // honest provenance signal) but never the raw relevance score — nomic cosine
+          // scores for genuinely relevant passages sit ~0.4-0.6, and surfacing e.g.
+          // "42%" primes the model to distrust correct context. Scores stay in the logs
+          // above for debugging.
           const contextText = trimmedDocs
-            .map((doc, idx) => `[Context ${idx + 1}] (Relevance: ${(doc.score * 100).toFixed(1)}%)\n${doc.text}`)
+            .map((doc, idx) => {
+              const title = doc.metadata?.full_title || doc.metadata?.article_title
+              const label = title ? `[Context ${idx + 1} — ${title}]` : `[Context ${idx + 1}]`
+              return `${label}\n${doc.text}`
+            })
             .join('\n\n')
 
           const systemMessage = {
diff --git a/admin/app/services/rag_service.ts b/admin/app/services/rag_service.ts
index 2a2110b..203913a 100644
--- a/admin/app/services/rag_service.ts
+++ b/admin/app/services/rag_service.ts
@@ -1011,6 +1011,29 @@ export class RagService {
           }
         }
 
+        // Boost when query keywords match the chunk's section/article heading. ZIM
+        // content carries this structural metadata (already fetched, no extra cost),
+        // and a query term appearing in a heading is a strong relevance signal that
+        // body-text matching alone misses. Same conservative, score-scaled, diminishing
+        // -returns shape as the boosts above, so it can't promote a weak match.
+        const headingText = [result.full_title, result.section_title, result.article_title]
+          .filter(Boolean)
+          .join(' ')
+          .toLowerCase()
+        if (headingText) {
+          const headingHits = queryKeywords.filter((kw) =>
+            headingText.includes(kw.toLowerCase())
+          ).length
+          if (headingHits > 0) {
+            const headingRatio = headingHits / Math.max(queryKeywords.length, 1)
+            const headingBoost = Math.sqrt(headingRatio) * 0.1 * result.score
+            logger.debug(
+              `[RAG] Heading match: ${headingHits}/${queryKeywords.length} - Boost: ${headingBoost.toFixed(3)}`
+            )
+            finalScore += headingBoost
+          }
+        }
+
         finalScore = Math.min(1.0, finalScore + keywordBoost)
 
         return {
diff --git a/admin/constants/ollama.ts b/admin/constants/ollama.ts
index 23df8f0..17268d8 100644
--- a/admin/constants/ollama.ts
+++ b/admin/constants/ollama.ts
@@ -86,18 +86,27 @@ export const SYSTEM_PROMPTS = {
  - Use tables when presenting structured data.
 `,
   rag_context: (context: string) => `
-You have access to relevant information from the knowledge base. This context has been retrieved based on semantic similarity to the user's question.
+Information has been retrieved from the NOMAD knowledge base that MAY be relevant to the
+user's question. It was selected by automated similarity search, which is imperfect — some
+or all of it may be unrelated to what the user actually asked.
 
 [Knowledge Base Context]
 ${context}
 
-IMPORTANT INSTRUCTIONS:
-1. If the user's question is directly related to the context above, use this information to provide accurate, detailed answers.
-2. Always cite or reference the context when using it (e.g., "According to the information available..." or "Based on the knowledge base...").
-3. If the context is only partially relevant, combine it with your general knowledge but be clear about what comes from the knowledge base.
-4. If the context is not relevant to the user's question, you can respond using your general knowledge without forcing the context into your answer. Do not mention the context if it's not relevant.
-5. Never fabricate information that isn't in the context or your training data.
-6. If you're unsure or you don't have enough information to answer the user's question, acknowledge the limitations.
+HOW TO ANSWER:
+1. First, silently judge whether the context genuinely addresses the user's question. Use
+   it ONLY when it really contains relevant information. Do not force a connection that
+   isn't there: poetic, narrative, tangential, or topically-unrelated passages are NOT
+   relevant just because they share a word with the question — ignore them.
+2. When the context is relevant, base your answer on it and answer directly and specifically.
+3. When the context does not actually address the question, ignore it completely and answer
+   from your own general knowledge. Do this silently — do not mention the knowledge base,
+   the context, or the fact that it lacked an answer, and do not apologize.
+4. Never narrate your retrieval or reasoning process. Do not write "according to Context 1",
+   "the context is unrelated, but", "I couldn't find specific context", or similar. Just
+   give the answer as if you simply knew it.
+5. Do not fabricate specifics (numbers, names, procedures) that are neither supported by
+   genuinely relevant context nor part of your reliable knowledge.
 
 Format your response using markdown for readability.
 `,