diff --git a/src/moderation/aiAnalyzer.ts b/src/moderation/aiAnalyzer.ts
index 6c09115..dc580a2 100644
--- a/src/moderation/aiAnalyzer.ts
+++ b/src/moderation/aiAnalyzer.ts
@@ -10,6 +10,8 @@ const queuedMessageIds = new Set<string>();
 let isProcessing = false;
 let activeRequests = 0;
 const MAX_CONCURRENT_REQUESTS = 1;
+const MAX_AI_REQUEST_TOKENS = 80_000;
+const AI_PROMPT_TOKEN_RESERVE = 6_000;
 
 interface ChatCompletionResponse {
   choices?: Array<{
@@ -30,6 +32,20 @@ function getAnalysisText(message: MessageRecord): string {
   return (message.edited_content || message.content || "").trim();
 }
 
+function estimateTokens(text: string): number {
+  return Math.ceil(text.length / 4);
+}
+
+function formatMessageForAnalysis(message: MessageRecord, index: number): string {
+  const text = getAnalysisText(message);
+  const time = new Date(message.created_at).toISOString();
+  return `${index + 1}. id=${message.id} time=${time} user=${message.username}: ${text}`;
+}
+
+function estimateMessageTokens(message: MessageRecord): number {
+  return estimateTokens(formatMessageForAnalysis(message, 0)) + 16;
+}
+
 async function fetchJson(url: string, init: RequestInit): Promise<unknown> {
   const controller = new AbortController();
   const timeout = setTimeout(() => controller.abort(), config.AI_ANALYSIS_TIMEOUT_MS);
@@ -86,7 +102,7 @@ function parseLLMAnalysis(content: string): LLMAnalysis {
   };
 }
 
-async function runLLMAnalysis(texts: string[]): Promise<{ results: LLMAnalysis[]; raw: unknown }> {
+async function runLLMAnalysis(messages: MessageRecord[]): Promise<{ results: LLMAnalysis[]; raw: unknown }> {
   const response = await retryWithBackoff(
     () => fetchJson(`${config.AI_LLM_BASE_URL}/chat/completions`, {
       method: "POST",
@@ -139,6 +155,8 @@ ATURAN KOMUNITAS LENGKAP:
 KONTEKS KOMUNITAS:
 - Ini grup bercanda/santai, jadi slang, candaan ringan, kata kasar ringan tanpa target, pesan pendek seperti "." atau "P", dan pertanyaan tidak jelas tetap CLEAN
 - Jangan beri WARN hanya karena pesan singkat, informal, ambigu, low-quality, atau kurang konteks
+- Pahami alur pembahasan antar pesan: pesan yang sendiri terlihat normal bisa WARN/FLAGGED jika dalam konteks percakapan sedang memancing konflik, menormalisasi pelanggaran, atau melanjutkan provokasi
+- Jangan menghukum orang yang sedang menasehati, menjelaskan bahaya, mengutip, atau menolak tindakan buruk; nilai maksud dan konteksnya
 - WARN hanya jika ada orang/kelompok yang diserang, dihina, diprovokasi, atau konflik mulai dipancing
 
 PENENTUAN STATUS:
@@ -150,7 +168,7 @@ Satu JSON object per pesan dalam array.`,
           },
           {
             role: "user",
-            content: `Analisis ${texts.length} pesan berikut:\n${texts.map((t, i) => `${i + 1}. ${t}`).join("\n")}`,
+            content: `Analisis ${messages.length} pesan berikut sebagai satu alur percakapan. Tetap kembalikan satu hasil per pesan dengan urutan yang sama:\n${messages.map(formatMessageForAnalysis).join("\n")}`,
           },
         ],
         temperature: 0.2,
@@ -188,7 +206,7 @@ Satu JSON object per pesan dalam array.`,
 
   // If batch parsing failed, parse as individual responses
   if (results.length === 0) {
-    results = texts.map(() => parseLLMAnalysis(content));
+    results = messages.map(() => parseLLMAnalysis(content));
   }
 
   return { results, raw: response };
@@ -197,15 +215,15 @@ Satu JSON object per pesan dalam array.`,
 async function analyzeAndStoreBatch(db: SqliteDatabase, messages: MessageRecord[]): Promise<void> {
   if (messages.length === 0) return;
 
-  const texts = messages.map(getAnalysisText).filter((t) => t.length > 0);
-  if (texts.length === 0) return;
+  const analyzableMessages = messages.filter((message) => getAnalysisText(message).length > 0);
+  if (analyzableMessages.length === 0) return;
 
   activeRequests++;
   try {
-    const { results, raw } = await runLLMAnalysis(texts);
+    const { results, raw } = await runLLMAnalysis(analyzableMessages);
 
-    for (let i = 0; i < messages.length; i++) {
-      const message = messages[i];
+    for (let i = 0; i < analyzableMessages.length; i++) {
+      const message = analyzableMessages[i];
       const result = results[i] || parseLLMAnalysis("");
 
       const row = updateMessageAIAnalysis(db, message.id, {
@@ -221,7 +239,7 @@ async function analyzeAndStoreBatch(db: SqliteDatabase, messages: MessageRecord[
     }
   } catch (error) {
     const errorMsg = error instanceof Error ? error.message : String(error);
-    for (const message of messages) {
+    for (const message of analyzableMessages) {
       const row = updateMessageAIAnalysis(db, message.id, {
         status: "error",
         flags: null,
@@ -243,24 +261,32 @@ async function drainQueue(db: SqliteDatabase): Promise<void> {
   if (isProcessing) return;
   isProcessing = true;
   try {
-    const BATCH_SIZE = 5;
+    const batchTokenLimit = MAX_AI_REQUEST_TOKENS - AI_PROMPT_TOKEN_RESERVE;
 
     while (queuedMessageIds.size > 0) {
-      // Wait if at max concurrent requests
       while (activeRequests >= MAX_CONCURRENT_REQUESTS) {
         await new Promise((resolve) => setTimeout(resolve, 100));
       }
 
-      // Collect batch of messages
       const batch: MessageRecord[] = [];
-      for (const messageId of queuedMessageIds) {
-        if (batch.length >= BATCH_SIZE) break;
-        queuedMessageIds.delete(messageId);
+      let tokenEstimate = 0;
+      for (const messageId of Array.from(queuedMessageIds)) {
         const message = getMessageById(db, messageId);
-        if (message) batch.push(message);
+        queuedMessageIds.delete(messageId);
+        if (!message) continue;
+
+        const messageTokens = estimateMessageTokens(message);
+        if (batch.length > 0 && tokenEstimate + messageTokens > batchTokenLimit) {
+          queuedMessageIds.add(messageId);
+          break;
+        }
+
+        batch.push(message);
+        tokenEstimate += messageTokens;
       }
 
       if (batch.length > 0) {
+        logger.info({ count: batch.length, tokenEstimate }, "Processing AI analysis batch");
         await analyzeAndStoreBatch(db, batch);
       }
     }
@@ -287,7 +313,7 @@ export function startPendingAIAnalysisWorker(db: SqliteDatabase): void {
   logger.info("AI analysis worker started");
   setInterval(() => {
     if (isProcessing) return;
-    const pendingMessages = getPendingAIAnalysisMessages(db, 3);
+    const pendingMessages = getPendingAIAnalysisMessages(db, 500);
     if (pendingMessages.length === 0) return;
     logger.info({ count: pendingMessages.length }, "Queueing pending AI analysis messages");
     for (const message of pendingMessages) {