diff --git a/frontend/src/components/ChatPanel.vue b/frontend/src/components/ChatPanel.vue
index b869043..d8290f2 100644
--- a/frontend/src/components/ChatPanel.vue
+++ b/frontend/src/components/ChatPanel.vue
@@ -29,8 +29,6 @@ const noteSearchResults = ref<{ id: number; title: string }[]>([]);
 const noteSearchLoading = ref(false);
 let noteSearchTimer: ReturnType<typeof setTimeout> | null = null;
 
-// Exclude tracking
-const excludedNoteIds = ref<Set<number>>(new Set());
 
 const streamingRendered = computed(() => {
   if (!store.streamingContent) return "";
@@ -65,11 +63,7 @@ async function sendMessage() {
   messageInput.value = "";
   resetTextareaHeight();
   scrollToBottom();
-  await store.sendMessage(
-    content,
-    contextNoteId,
-    excludedNoteIds.value.size ? [...excludedNoteIds.value] : undefined,
-  );
+  await store.sendMessage(content, contextNoteId);
   scrollToBottom();
 }
 
@@ -151,10 +145,6 @@ function removeAttachedNote() {
 function promoteAutoNote(note: { id: number; title: string }) {
   attachedNote.value = note;
 }
-
-function excludeAutoNote(noteId: number) {
-  excludedNoteIds.value = new Set([...excludedNoteIds.value, noteId]);
-}
 </script>
 
 <template>
@@ -198,11 +188,6 @@ function excludeAutoNote(noteId: number) {
                 @click="promoteAutoNote(note)"
                 title="Attach for next message"
               >+</button>
-              <button
-                class="context-pill-btn exclude"
-                @click="excludeAutoNote(note.id)"
-                title="Exclude from auto-search"
-              >&times;</button>
             </span>
           </div>
 
@@ -463,9 +448,6 @@ function excludeAutoNote(noteId: number) {
 .context-pill-btn.promote:hover {
   color: var(--color-primary);
 }
-.context-pill-btn.exclude:hover {
-  color: var(--color-danger, #e74c3c);
-}
 
 /* Input wrapper */
 .panel-input-wrapper {
diff --git a/frontend/src/stores/chat.ts b/frontend/src/stores/chat.ts
index 29a2dd6..63fb236 100644
--- a/frontend/src/stores/chat.ts
+++ b/frontend/src/stores/chat.ts
@@ -119,7 +119,7 @@ export const useChatStore = defineStore("chat", () => {
   async function sendMessage(
     content: string,
     contextNoteId?: number | null,
-    excludeNoteIds?: number[],
+    includeNoteIds?: number[],
     think = false,
     contextNoteTitle?: string,
   ) {
@@ -167,7 +167,7 @@ export const useChatStore = defineStore("chat", () => {
         {
           content,
           context_note_id: contextNoteId,
-          exclude_note_ids: excludeNoteIds?.length ? excludeNoteIds : undefined,
+          include_note_ids: includeNoteIds?.length ? includeNoteIds : undefined,
           think,
         },
       );
diff --git a/frontend/src/views/ChatView.vue b/frontend/src/views/ChatView.vue
index 46e74c8..0486ac0 100644
--- a/frontend/src/views/ChatView.vue
+++ b/frontend/src/views/ChatView.vue
@@ -30,11 +30,12 @@ const noteSearchResults = ref<{ id: number; title: string }[]>([]);
 const noteSearchLoading = ref(false);
 let noteSearchTimer: ReturnType<typeof setTimeout> | null = null;
 
-// Exclude tracking (session-scoped per conversation)
-const excludedNoteIds = ref<Set<number>>(new Set());
+// Explicitly included notes (user clicked "+ include" in sidebar)
+const includedNoteIds = ref<Set<number>>(new Set());
+const includedNotes = ref<{ id: number; title: string }[]>([]);
 
-// Persistent context notes — populated as assistant responds, cleared on conv change
-const contextNotes = ref<{ id: number; title: string }[]>([]);
+// Suggested notes — auto-found by search, not yet included
+const suggestedNotes = ref<{ id: number; title: string }[]>([]);
 
 let prevConvId: number | null = null;
 
@@ -91,8 +92,9 @@ watch(convId, async (newId) => {
   }
   prevConvId = newId ?? null;
 
-  excludedNoteIds.value = new Set();
-  contextNotes.value = [];
+  includedNoteIds.value = new Set();
+  includedNotes.value = [];
+  suggestedNotes.value = [];
   attachedNote.value = null;
   store.lastContextMeta = null;
   if (newId) {
@@ -117,12 +119,11 @@ watch(
   () => store.lastContextMeta?.auto_notes,
   (newNotes) => {
     if (!newNotes) return;
-    const excluded = excludedNoteIds.value;
-    const existing = new Set(contextNotes.value.map((n) => n.id));
+    const alreadyIncluded = includedNoteIds.value;
+    const alreadySuggested = new Set(suggestedNotes.value.map((n) => n.id));
     for (const note of newNotes) {
-      if (!excluded.has(note.id) && !existing.has(note.id)) {
-        contextNotes.value.push(note);
-        existing.add(note.id);
+      if (!alreadyIncluded.has(note.id) && !alreadySuggested.has(note.id)) {
+        suggestedNotes.value.push(note);
       }
     }
   }
@@ -180,7 +181,7 @@ async function sendMessage() {
   await store.sendMessage(
     content,
     contextNoteId,
-    excludedNoteIds.value.size ? [...excludedNoteIds.value] : undefined,
+    includedNoteIds.value.size ? [...includedNoteIds.value] : undefined,
     true, // enable thinking in the full chat view
     contextNoteTitle,
   );
@@ -282,9 +283,20 @@ function removeAttachedNote() {
   attachedNote.value = null;
 }
 
-function excludeAutoNote(noteId: number) {
-  excludedNoteIds.value = new Set([...excludedNoteIds.value, noteId]);
-  contextNotes.value = contextNotes.value.filter((n) => n.id !== noteId);
+function includeNote(note: { id: number; title: string }) {
+  if (includedNoteIds.value.has(note.id)) return;
+  includedNoteIds.value = new Set([...includedNoteIds.value, note.id]);
+  includedNotes.value.push(note);
+  suggestedNotes.value = suggestedNotes.value.filter((n) => n.id !== note.id);
+}
+
+function removeIncludedNote(noteId: number) {
+  includedNoteIds.value = new Set([...includedNoteIds.value].filter((id) => id !== noteId));
+  const removed = includedNotes.value.find((n) => n.id === noteId);
+  includedNotes.value = includedNotes.value.filter((n) => n.id !== noteId);
+  if (removed && !suggestedNotes.value.some((n) => n.id === noteId)) {
+    suggestedNotes.value.push(removed);
+  }
 }
 
 // Keyboard shortcuts
@@ -420,26 +432,40 @@ onUnmounted(() => {
             </div>
           </div>
 
-          <!-- Persistent context sidebar -->
-          <aside v-if="contextNotes.length || attachedNote" class="context-sidebar">
-            <div class="context-sidebar-header">In Context</div>
+          <!-- Context sidebar -->
+          <aside v-if="includedNotes.length || suggestedNotes.length || attachedNote" class="context-sidebar">
+            <!-- IN CONTEXT section -->
+            <template v-if="attachedNote || includedNotes.length">
+              <div class="context-sidebar-header">In Context</div>
 
-            <!-- Manually attached note — pinned until sent -->
-            <div v-if="attachedNote" class="context-note context-note-pinned">
-              <span class="context-note-icon" title="Attached for this message">📌</span>
-              <router-link :to="`/notes/${attachedNote.id}`" class="context-note-name">
-                {{ attachedNote.title }}
-              </router-link>
-              <button class="context-note-remove" @click="removeAttachedNote" title="Remove">&times;</button>
-            </div>
+              <!-- Manually attached note — pinned until sent -->
+              <div v-if="attachedNote" class="context-note context-note-pinned">
+                <span class="context-note-icon" title="Attached for this message">📌</span>
+                <router-link :to="`/notes/${attachedNote.id}`" class="context-note-name">
+                  {{ attachedNote.title }}
+                </router-link>
+                <button class="context-note-remove" @click="removeAttachedNote" title="Remove">&times;</button>
+              </div>
 
-            <!-- Auto-found notes — persist across turns -->
-            <div v-for="note in contextNotes" :key="note.id" class="context-note">
-              <router-link :to="`/notes/${note.id}`" class="context-note-name">
-                {{ note.title }}
-              </router-link>
-              <button class="context-note-remove" @click="excludeAutoNote(note.id)" title="Remove from context">&times;</button>
-            </div>
+              <!-- Explicitly included notes -->
+              <div v-for="note in includedNotes" :key="note.id" class="context-note context-note-included">
+                <router-link :to="`/notes/${note.id}`" class="context-note-name">
+                  {{ note.title }}
+                </router-link>
+                <button class="context-note-remove" @click="removeIncludedNote(note.id)" title="Remove from context">&times;</button>
+              </div>
+            </template>
+
+            <!-- SUGGESTED section -->
+            <template v-if="suggestedNotes.length">
+              <div class="context-sidebar-header" :class="{ 'context-sidebar-header-gap': attachedNote || includedNotes.length }">Suggested</div>
+              <div v-for="note in suggestedNotes" :key="note.id" class="context-note context-note-suggested">
+                <router-link :to="`/notes/${note.id}`" class="context-note-name">
+                  {{ note.title }}
+                </router-link>
+                <button class="context-note-add" @click="includeNote(note)" title="Add to context">+</button>
+              </div>
+            </template>
           </aside>
         </div>
 
@@ -729,6 +755,15 @@ onUnmounted(() => {
 .context-note-name:hover {
   color: var(--color-primary);
 }
+.context-sidebar-header-gap {
+  margin-top: 0.6rem;
+}
+.context-note-included {
+  border-color: var(--color-primary);
+}
+.context-note-suggested {
+  opacity: 0.85;
+}
 .context-note-remove {
   background: none;
   border: none;
@@ -742,6 +777,20 @@ onUnmounted(() => {
 .context-note-remove:hover {
   color: var(--color-danger, #e74c3c);
 }
+.context-note-add {
+  background: none;
+  border: none;
+  cursor: pointer;
+  color: var(--color-primary);
+  font-size: 1.1rem;
+  font-weight: 700;
+  line-height: 1;
+  padding: 0 0.1rem;
+  flex-shrink: 0;
+}
+.context-note-add:hover {
+  opacity: 0.75;
+}
 .messages-inner {
   margin-top: auto;
 }
diff --git a/src/fabledassistant/routes/chat.py b/src/fabledassistant/routes/chat.py
index c91d56e..6cbdc19 100644
--- a/src/fabledassistant/routes/chat.py
+++ b/src/fabledassistant/routes/chat.py
@@ -113,7 +113,7 @@ async def send_message_route(conv_id: int):
     if not content:
         return jsonify({"error": "content is required"}), 400
     context_note_id = data.get("context_note_id")
-    exclude_note_ids = data.get("exclude_note_ids") or []
+    include_note_ids = data.get("include_note_ids") or []
     think = bool(data.get("think", False))
 
     # Reject if generation already running for this conversation
@@ -142,7 +142,7 @@ async def send_message_route(conv_id: int):
         buf, history, model,
         uid, conv_id, conv.title, content,
         context_note_id=context_note_id,
-        exclude_note_ids=exclude_note_ids,
+        include_note_ids=include_note_ids,
         think=think,
     ))
 
diff --git a/src/fabledassistant/services/generation_buffer.py b/src/fabledassistant/services/generation_buffer.py
index 2c59dc3..ce289ad 100644
--- a/src/fabledassistant/services/generation_buffer.py
+++ b/src/fabledassistant/services/generation_buffer.py
@@ -75,26 +75,6 @@ class GenerationBuffer:
 # Module-level singleton registry
 _buffers: dict[int | str, GenerationBuffer] = {}
 
-# Per-conversation note context cache — maps conv_id → sorted list of note IDs.
-# Stores the note IDs that were last included in the system prompt so that
-# subsequent turns in the same conversation can reuse them, stabilizing the
-# system prompt prefix and improving Ollama's KV cache hit rate.
-_conv_note_cache: dict[int, list[int]] = {}
-
-
-def get_conv_note_cache(conv_id: int) -> list[int]:
-    """Return cached note IDs for a conversation (empty list if none)."""
-    return list(_conv_note_cache.get(conv_id, []))
-
-
-def set_conv_note_cache(conv_id: int, note_ids: list[int]) -> None:
-    """Store note IDs to reuse on the next turn of this conversation."""
-    _conv_note_cache[conv_id] = list(note_ids)
-
-
-def clear_conv_note_cache(conv_id: int) -> None:
-    """Invalidate the note cache for a conversation (e.g. after a note write)."""
-    _conv_note_cache.pop(conv_id, None)
 _cleanup_task: asyncio.Task | None = None
 _GRACE_PERIOD = 60.0  # seconds to keep completed buffers
 
diff --git a/src/fabledassistant/services/generation_task.py b/src/fabledassistant/services/generation_task.py
index 355250f..93793a1 100644
--- a/src/fabledassistant/services/generation_task.py
+++ b/src/fabledassistant/services/generation_task.py
@@ -21,9 +21,6 @@ from fabledassistant.models.conversation import Message
 from fabledassistant.services.generation_buffer import (
     GenerationBuffer,
     GenerationState,
-    clear_conv_note_cache,
-    get_conv_note_cache,
-    set_conv_note_cache,
 )
 from fabledassistant.services.llm import ChatChunk, build_context, generate_completion, stream_chat, stream_chat_with_tools, summarize_history_for_context
 from fabledassistant.services.chat import update_conversation_title
@@ -97,39 +94,6 @@ _TOOL_ACTIONS: dict[str, str] = {
 }
 
 
-async def _generate_acknowledgment(user_content: str, tool_name: str, model: str) -> str:
-    """Generate a brief conversational acknowledgment that runs in parallel with tool execution.
-
-    Uses the intent model (small, fast, already in VRAM) so the sentence is ready
-    within ~200-400ms. Returned string includes a trailing double-newline so the
-    main LLM response starts on a new paragraph.
-    """
-    action = _TOOL_ACTIONS.get(tool_name, "work on that")
-    messages = [
-        {
-            "role": "system",
-            "content": (
-                "You are a helpful assistant. Write ONE short, natural sentence acknowledging "
-                "what you are about to do. Vary your phrasing — do not always start with "
-                "'Let me'. Be warm and conversational. Do not answer the question yet. "
-                "Output only the sentence, nothing else."
-            ),
-        },
-        {
-            "role": "user",
-            "content": f"User said: {user_content}\nYou are about to: {action}",
-        },
-    ]
-    try:
-        ack = await generate_completion(messages, model, max_tokens=40)
-        ack = ack.strip()
-        if ack:
-            return ack + "\n\n"
-    except Exception:
-        logger.warning("Failed to generate acknowledgment", exc_info=True)
-    return ""
-
-
 async def _generate_title(messages: list[dict], model: str) -> str:
     """Ask the LLM for a concise conversation title."""
     # Build conversation text like summarize_conversation_as_note
@@ -175,26 +139,6 @@ async def _update_message(
         await session.commit()
 
 
-async def _drain_queue(
-    prefetched: list[ChatChunk],
-    queue: asyncio.Queue,
-) -> AsyncGenerator[ChatChunk, None]:
-    """Yield pre-fetched chunks then drain remaining chunks from the queue.
-
-    A None sentinel in the queue signals the stream is finished.
-    A BaseException in the queue is re-raised so callers see the error.
-    """
-    for chunk in prefetched:
-        yield chunk
-    while True:
-        item = await queue.get()
-        if item is None:
-            break
-        if isinstance(item, BaseException):
-            raise item
-        yield item
-
-
 async def _stream_with_retry(
     messages: list[dict],
     model: str,
@@ -239,7 +183,7 @@ async def run_generation(
     conv_title: str,
     user_content: str,
     context_note_id: int | None = None,
-    exclude_note_ids: list[int] | None = None,
+    include_note_ids: list[int] | None = None,
     think: bool = False,
 ) -> None:
     """Stream LLM response into buffer with periodic DB flushes."""
@@ -266,17 +210,13 @@ async def run_generation(
         buf.append_event("status", {"status": "Summarizing conversation history..."})
         history_to_use, history_summary = await summarize_history_for_context(history, intent_model)
 
-    # Phase 3: Build context. Start intent classification concurrently when tools
-    # are available so it runs in parallel with the embedding/DB work in build_context.
-    # We only block on context (need messages to stream) — intent result is consumed
-    # later via a race with the first streaming token.
-    cached_note_ids = get_conv_note_cache(conv_id) or None
-
+    # Phase 3: Build context and start intent classification in parallel.
+    # We block on context (need messages to stream) — intent is consumed
+    # after context is ready, at the start of round 0.
     context_task = asyncio.create_task(build_context(
         user_id, history_to_use, context_note_id, user_content,
-        exclude_note_ids=exclude_note_ids,
         history_summary=history_summary,
-        cached_note_ids=cached_note_ids,
+        include_note_ids=include_note_ids,
     ))
 
     intent_task: asyncio.Task[IntentResult] | None = None
@@ -292,11 +232,6 @@ async def run_generation(
 
     messages, context_meta = await context_task
 
-    # Update the note cache with whatever notes ended up in context.
-    new_note_ids = context_meta.get("auto_note_ids") or []
-    if new_note_ids:
-        set_conv_note_cache(conv_id, new_note_ids)
-
     # Emit context event
     buf.append_event("context", {"context": context_meta})
 
@@ -319,89 +254,27 @@ async def run_generation(
             round_tool_calls: list[dict] = []
             logger.info("Generation round %d started for conv %d (model=%s)", _round, conv_id, model)
 
-            # --- Round 0 with tools: optimistic streaming ---
-            # Start the main stream immediately (into a queue) while the intent
-            # classifier finishes in the background. Race the two:
-            #   • Intent wins before first token → check for tool call, cancel stream if needed
-            #   • First token wins → discard intent, stream has already started
+            # --- Round 0 with tools: intent-first pipeline ---
+            # Wait for intent result, then act immediately. The ack sentence
+            # (embedded in the intent JSON) is streamed at TTFT (~400ms), then
+            # the tool runs while the user is reading it.
             if _round == 0 and tools and intent_task is not None:
-                stream_queue: asyncio.Queue[ChatChunk | BaseException | None] = asyncio.Queue(maxsize=256)
+                intent = await intent_task
+                timing["intent_ms"] = int((time.monotonic() - t_intent) * 1000)
 
-                async def _fill_queue() -> None:
-                    # Retry on Ollama 500 (model cold-loading race) up to 2 times.
-                    last_exc: BaseException | None = None
-                    for attempt in range(3):
-                        if attempt > 0:
-                            wait = 3.0 * attempt
-                            logger.warning(
-                                "Ollama stream 500 (attempt %d/3), retrying in %.0fs", attempt, wait
-                            )
-                            await asyncio.sleep(wait)
-                        try:
-                            async for c in stream_chat_with_tools(messages, model, tools=tools, think=think):
-                                await stream_queue.put(c)
-                            last_exc = None
-                            break
-                        except httpx.HTTPStatusError as exc:
-                            last_exc = exc
-                            if exc.response.status_code != 500:
-                                break  # non-500 errors are not retryable
-                        except BaseException as exc:
-                            last_exc = exc
-                            break
-                    if last_exc is not None:
-                        await stream_queue.put(last_exc)
-                    await stream_queue.put(None)
-
-                stream_fill_task = asyncio.create_task(_fill_queue())
-                buf.append_event("status", {"status": "Generating response..."})
-
-                queue_peek = asyncio.create_task(stream_queue.get())
-                race_done, _ = await asyncio.wait(
-                    {intent_task, queue_peek},
-                    return_when=asyncio.FIRST_COMPLETED,
-                )
-
-                intent = IntentResult()
-                prefetched: list[ChatChunk] = []
-                use_tool_path = False
-
-                if intent_task in race_done and queue_peek not in race_done:
-                    # Intent finished before the first streaming token arrived.
-                    timing["intent_ms"] = int((time.monotonic() - t_intent) * 1000)
-                    intent = intent_task.result()
-
-                    if intent.should_execute:
-                        # Cancel the optimistic stream — we'll execute a tool instead.
-                        stream_fill_task.cancel()
-                        queue_peek.cancel()
-                        use_tool_path = True
-                    else:
-                        # No tool needed — collect the first chunk and keep streaming.
-                        first = await queue_peek
-                        if isinstance(first, BaseException):
-                            raise first
-                        if first is not None:
-                            prefetched.append(first)
-                else:
-                    # Stream produced a token before intent finished (or simultaneously).
-                    # Discard intent — the response is already on its way.
-                    if not intent_task.done():
-                        intent_task.cancel()
-                    else:
-                        timing["intent_ms"] = int((time.monotonic() - t_intent) * 1000)
-
-                    first = queue_peek.result() if queue_peek in race_done else await queue_peek
-                    if isinstance(first, BaseException):
-                        raise first
-                    if first is not None:
-                        prefetched.append(first)
-
-                if use_tool_path:
-                    # === Tool path (intent won the race) ===
+                if intent.should_execute:
                     tool_name = intent.tool_name
-                    confirmed = True
 
+                    # Stream ack immediately — this becomes TTFT
+                    ack_text = (intent.ack or "").strip()
+                    if ack_text:
+                        ack_with_newline = ack_text + "\n\n"
+                        buf.append_event("chunk", {"chunk": ack_with_newline})
+                        buf.content_so_far += ack_with_newline
+                        if timing["ttft_ms"] is None:
+                            timing["ttft_ms"] = int((time.monotonic() - t_start) * 1000)
+
+                    confirmed = True
                     if tool_name in _WRITE_TOOLS:
                         loop = asyncio.get_running_loop()
                         confirm_future: asyncio.Future = loop.create_future()
@@ -449,24 +322,11 @@ async def run_generation(
 
                     if confirmed:
                         buf.append_event("status", {"status": f"{_TOOL_LABELS.get(tool_name, 'Working')}..."})
-
                         t_tool = time.monotonic()
-                        result, ack_text = await asyncio.gather(
-                            execute_tool(user_id, tool_name, intent.arguments),
-                            _generate_acknowledgment(user_content, tool_name, intent_model),
-                        )
+                        result = await execute_tool(user_id, tool_name, intent.arguments)
                         timing["tools"].append({"name": tool_name, "ms": int((time.monotonic() - t_tool) * 1000)})
                         logger.info("Intent-routed tool %s result: success=%s", tool_name, result.get("success"))
 
-                        if ack_text:
-                            buf.append_event("chunk", {"chunk": ack_text})
-                            buf.content_so_far += ack_text
-                            if timing["ttft_ms"] is None:
-                                timing["ttft_ms"] = int((time.monotonic() - t_start) * 1000)
-
-                        if result.get("success") and tool_name in {"create_task", "create_note", "update_note", "delete_note", "delete_task"}:
-                            clear_conv_note_cache(conv_id)
-
                         tool_record = {
                             "function": tool_name,
                             "arguments": intent.arguments,
@@ -478,35 +338,23 @@ async def run_generation(
 
                         messages.append({
                             "role": "assistant",
-                            "content": ack_text,
+                            "content": buf.content_so_far,
                             "tool_calls": [
                                 {"function": {"name": tool_name, "arguments": intent.arguments}}
                             ],
                         })
-                        messages.append({
-                            "role": "tool",
-                            "content": json.dumps(result),
-                        })
-                        continue  # Round 1: stream the response incorporating tool result
+                        messages.append({"role": "tool", "content": json.dumps(result)})
+                        continue  # Round 1: stream response with tool result
 
-                    # Declined write tool — fall through to stream a fresh response.
+                    # Declined write tool — fall through to fresh stream.
                     if cancelled:
                         break
 
-                # === Stream path for round 0 ===
-                # Either intent said no-tool, or a write tool was declined.
-                # For no-tool: drain the already-started queue (prefetched + remaining).
-                # For declined: start a fresh stream (queue was cancelled).
+                # No tool (or declined write tool): stream directly, no queue.
                 buf.append_event("status", {"status": "Generating response..."})
                 t_stream = time.monotonic()
 
-                if use_tool_path:
-                    # Declined write tool — the optimistic stream was cancelled, start fresh.
-                    stream_source: AsyncGenerator = _stream_with_retry(messages, model, tools, think)
-                else:
-                    stream_source = _drain_queue(prefetched, stream_queue)
-
-                async for chunk in stream_source:
+                async for chunk in _stream_with_retry(messages, model, tools, think):
                     if buf.cancel_event.is_set():
                         cancelled = True
                         break
@@ -541,9 +389,6 @@ async def run_generation(
                             timing["tools"].append({"name": tool_name, "ms": int((time.monotonic() - t_tool) * 1000)})
                             logger.info("Tool %s result: success=%s", tool_name, result.get("success"))
 
-                            if result.get("success") and tool_name in {"create_task", "create_note", "update_note", "delete_note", "delete_task"}:
-                                clear_conv_note_cache(conv_id)
-
                             tool_record = {
                                 "function": tool_name,
                                 "arguments": arguments,
@@ -618,9 +463,6 @@ async def run_generation(
                         timing["tools"].append({"name": tool_name, "ms": int((time.monotonic() - t_tool) * 1000)})
                         logger.info("Tool %s result: success=%s", tool_name, result.get("success"))
 
-                        if result.get("success") and tool_name in {"create_task", "create_note", "update_note", "delete_note", "delete_task"}:
-                            clear_conv_note_cache(conv_id)
-
                         tool_record = {
                             "function": tool_name,
                             "arguments": arguments,
diff --git a/src/fabledassistant/services/intent.py b/src/fabledassistant/services/intent.py
index c54233d..abc5efd 100644
--- a/src/fabledassistant/services/intent.py
+++ b/src/fabledassistant/services/intent.py
@@ -22,6 +22,7 @@ class IntentResult:
     tool_name: str | None = None  # None = no tool, just chat
     arguments: dict = field(default_factory=dict)
     confidence: str = "high"  # "high", "medium", or "low"
+    ack: str | None = None  # One-sentence acknowledgment to stream immediately
 
     @property
     def should_execute(self) -> bool:
@@ -61,8 +62,8 @@ Available tools:
 {tool_summary}
 
 Respond with ONLY a JSON object, no other text:
-- If a tool should be called: {{"tool": "tool_name", "arguments": {{...}}, "confidence": "high"|"medium"|"low"}}
-- If it's general chat: {{"tool": null, "confidence": "high"}}
+- If a tool should be called: {{"tool": "tool_name", "arguments": {{...}}, "confidence": "high"|"medium"|"low", "ack": "One short sentence describing what you're about to do."}}
+- If it's general chat: {{"tool": null, "confidence": "high", "ack": null}}
 
 Confidence levels:
 - "high": the intent is clear and all required arguments are unambiguous
@@ -99,6 +100,7 @@ Rules:
 - "read", "open", "show me", "what does X say", "display", "pull up" a specific note → use get_note with query=<note name>.
 - "list my notes", "show notes", "recent notes", "browse notes", "notes tagged X" → use list_notes (with optional q or tags).
 - "tag X with Y", "add tag Y to X", "untag Y from X", "remove tag Y from X" → use update_note with tags=[Y] and tag_mode="add" or "remove".
+- "ack": one short, natural sentence confirming the action (tool path only). Vary phrasing — do not always start with "Let me". Omit (null) for chat-only responses.
 - Do NOT wrap the JSON in markdown code fences."""
 
 
@@ -142,7 +144,7 @@ async def classify_intent(
     messages.append({"role": "user", "content": user_message})
 
     try:
-        raw = await generate_completion(messages, model, max_tokens=200)
+        raw = await generate_completion(messages, model, max_tokens=350)
     except Exception:
         logger.warning("Intent classification LLM call failed", exc_info=True)
         return IntentResult()
@@ -192,11 +194,15 @@ def _parse_intent(raw: str, tools: list[dict]) -> IntentResult:
     if not isinstance(arguments, dict):
         arguments = {}
 
+    ack = parsed.get("ack") or None
+    if ack is not None:
+        ack = ack.strip() or None
+
     logger.info(
         "Intent classified: tool=%s, confidence=%s, args=%s",
         tool_name, confidence, json.dumps(arguments)[:200],
     )
-    return IntentResult(tool_name=tool_name, arguments=arguments, confidence=confidence)
+    return IntentResult(tool_name=tool_name, arguments=arguments, confidence=confidence, ack=ack)
 
 
 def _try_json(text: str) -> dict | list | None:
diff --git a/src/fabledassistant/services/llm.py b/src/fabledassistant/services/llm.py
index 1bb20c4..a9a7a84 100644
--- a/src/fabledassistant/services/llm.py
+++ b/src/fabledassistant/services/llm.py
@@ -1,3 +1,4 @@
+import asyncio
 import json
 import logging
 import re
@@ -178,21 +179,41 @@ async def stream_chat_with_tools(
 
 
 async def generate_completion(messages: list[dict], model: str, max_tokens: int = 4096) -> str:
-    """Non-streaming chat completion, returns full response text."""
-    async with httpx.AsyncClient(timeout=httpx.Timeout(1800.0, connect=30.0, read=300.0)) as client:
-        resp = await client.post(
-            f"{Config.OLLAMA_URL}/api/chat",
-            json={
-                "model": model,
-                "messages": messages,
-                "stream": False,
-                "think": False,
-                "options": {"num_predict": max_tokens},
-            },
-        )
-        resp.raise_for_status()
-        data = resp.json()
-        return data.get("message", {}).get("content", "")
+    """Non-streaming chat completion, returns full response text.
+
+    Retries up to 2 times on Ollama 500 errors (cold model loading race).
+    """
+    last_exc: Exception | None = None
+    for attempt in range(3):
+        if attempt > 0:
+            delay = 3.0 * attempt
+            logger.warning(
+                "generate_completion 500 (attempt %d/3), retrying in %.0fs", attempt, delay
+            )
+            await asyncio.sleep(delay)
+        try:
+            async with httpx.AsyncClient(timeout=httpx.Timeout(1800.0, connect=30.0, read=300.0)) as client:
+                resp = await client.post(
+                    f"{Config.OLLAMA_URL}/api/chat",
+                    json={
+                        "model": model,
+                        "messages": messages,
+                        "stream": False,
+                        "think": False,
+                        "options": {"num_predict": max_tokens},
+                    },
+                )
+                resp.raise_for_status()
+                data = resp.json()
+                return data.get("message", {}).get("content", "")
+        except httpx.HTTPStatusError as exc:
+            last_exc = exc
+            if exc.response.status_code != 500:
+                break
+        except Exception as exc:
+            last_exc = exc
+            break
+    raise last_exc
 
 
 async def fetch_url_content(url: str) -> str:
@@ -307,7 +328,7 @@ async def build_context(
     user_message: str,
     exclude_note_ids: list[int] | None = None,
     history_summary: str | None = None,
-    cached_note_ids: list[int] | None = None,
+    include_note_ids: list[int] | None = None,
 ) -> tuple[list[dict], dict]:
     """Build messages array for Ollama with system prompt and context.
 
@@ -376,64 +397,58 @@ async def build_context(
                 f"--- End Note ---"
             )
 
-    # Find related notes to inject into context.
-    # Priority: (1) use cached note IDs from a previous turn in this conversation
-    #           (2) try semantic search via nomic-embed-text
-    #           (3) fall back to keyword search
-    # The cache stabilises the system prompt prefix so Ollama's KV cache stays warm.
+    # Search for related notes to populate sidebar candidates.
+    # Results are NOT injected into the system prompt automatically — this keeps
+    # the system prompt prefix stable so Ollama's KV cache can reuse prefill state.
+    # Users can explicitly include notes via the sidebar (include_note_ids).
     search_exclude = set(exclude_set)
     if current_note_id:
         search_exclude.add(current_note_id)
 
     found_notes = []
-    if cached_note_ids:
-        # Load the same notes as last turn — keeps system prompt prefix identical.
-        try:
-            from fabledassistant.services.notes import get_note as _get_note
-            for nid in cached_note_ids:
-                if nid not in search_exclude:
-                    n = await _get_note(user_id, nid)
-                    if n:
-                        found_notes.append(n)
-        except Exception:
-            logger.warning("Failed to load cached notes for context", exc_info=True)
-            found_notes = []
+    # Try semantic search first; fall back to keyword search on failure / no results.
+    try:
+        from fabledassistant.services.embeddings import semantic_search_notes
+        found_notes = await semantic_search_notes(
+            user_id, user_message, exclude_ids=search_exclude or None, limit=3
+        )
+    except Exception:
+        logger.warning("Semantic note search failed, falling back to keyword search", exc_info=True)
 
     if not found_notes:
-        # Try semantic search first; fall back to keyword search on failure / no results.
-        try:
-            from fabledassistant.services.embeddings import semantic_search_notes
-            found_notes = await semantic_search_notes(
-                user_id, user_message, exclude_ids=search_exclude or None, limit=3
-            )
-        except Exception:
-            logger.warning("Semantic note search failed, falling back to keyword search", exc_info=True)
+        keywords = _extract_keywords(user_message)
+        if keywords:
+            try:
+                found_notes = await search_notes_for_context(
+                    user_id, keywords, exclude_ids=search_exclude or None, limit=3
+                )
+            except Exception:
+                logger.warning("Failed to search notes for context", exc_info=True)
 
-        if not found_notes:
-            keywords = _extract_keywords(user_message)
-            if keywords:
-                try:
-                    found_notes = await search_notes_for_context(
-                        user_id, keywords, exclude_ids=search_exclude or None, limit=3
-                    )
-                except Exception:
-                    logger.warning("Failed to search notes for context", exc_info=True)
-
-    if found_notes:
-        snippets: list[str] = []
-        for n in found_notes:
-            body_preview = n.body[:2000] if n.body else ""
-            snippets.append(f"- {n.title}: {body_preview}")
-            context_meta["auto_notes"].append({"id": n.id, "title": n.title})
-        system_parts.append(
-            "\n\n--- Related Notes ---\n"
-            + "\n".join(snippets)
-            + "\n--- End Related Notes ---"
-        )
-
-    # Expose note IDs so the caller can update the per-conversation cache.
+    # Populate sidebar candidates (never auto-injected).
+    for n in found_notes:
+        context_meta["auto_notes"].append({"id": n.id, "title": n.title})
     context_meta["auto_note_ids"] = [n.id for n in found_notes]
 
+    # Inject explicitly included notes (user opted in via sidebar click).
+    if include_note_ids:
+        from fabledassistant.services.notes import get_note as _get_note
+        included_snippets: list[str] = []
+        for nid in include_note_ids:
+            try:
+                n = await _get_note(user_id, nid)
+                if n:
+                    body_preview = n.body[:2000] if n.body else ""
+                    included_snippets.append(f"- {n.title}: {body_preview}")
+            except Exception:
+                logger.warning("Failed to load included note %d for context", nid, exc_info=True)
+        if included_snippets:
+            system_parts.append(
+                "\n\n--- Included Notes ---\n"
+                + "\n".join(included_snippets)
+                + "\n--- End Included Notes ---"
+            )
+
     # Fetch URL content from user message
     urls = _find_urls(user_message)
     for url in urls[:2]:  # Limit to 2 URLs
diff --git a/summary.md b/summary.md
index 16f3a3c..4d4620d 100644
--- a/summary.md
+++ b/summary.md
@@ -12,7 +12,7 @@
 >    Include file-level details in the commit body when the change is non-trivial.
 
 ## Last Updated
-2026-02-26 — Phase 20: Dedicated tag field (chip input), tags no longer extracted from body
+2026-02-26 — Phase 21: Intent-first pipeline, visible acknowledgment, KV-stable system prompt
 
 ## Project Overview
 Fabled Assistant is a self-hosted note-taking and task-tracking application with
@@ -75,11 +75,15 @@ for AI-assisted features.
   reconnection support. Frontend uses `fetch()` + `ReadableStream`. Simpler than
   WebSockets; Quart supports async generators natively for SSE.
 - **Context building server-side:** Backend fetches URL content and searches notes —
-  frontend just sends the message text + optional note ID + optional exclude list.
+  frontend sends the message text + optional context note ID + optional `include_note_ids`.
   Keyword extraction uses simple word splitting with stopword filtering (no embeddings).
   `build_context()` returns `(messages, context_meta)` tuple; metadata includes
   auto-found note IDs/titles sent to frontend via SSE `context` event before streaming.
   Multi-word search splits terms into per-word ILIKE with AND logic (not adjacent match).
+  Auto-found notes populate the sidebar but are **not** injected into the system prompt
+  automatically — users must click `+` in the sidebar to include them (stable system
+  prompt prefix enables Ollama KV cache reuse). Explicitly included notes appear as
+  `--- Included Notes ---` in the system prompt.
 - **Reverse proxy required for production:** The app does not terminate TLS. A
   reverse proxy (Nginx/Traefik/Caddy) must sit in front of port 5000. Do not
   expose the app directly to the internet.
@@ -283,8 +287,8 @@ fabledassistant/
 │       │   ├── llm.py           # Ollama interaction: build_context with user_id, streaming (stream_chat + stream_chat_with_tools), ChatChunk dataclass, URL fetching; uses Config.OLLAMA_NUM_CTX for KV cache window
 │       │   ├── chat.py          # Conversation CRUD with user_id isolation, add_message, save/summarize as note (LLM-titled, chat-tagged)
 │       │   ├── generation_buffer.py # In-memory SSE event buffer with cancel_event, reconnect support, auto-cleanup; supports chat (int keys) and assist (string keys)
-│       │   ├── generation_task.py   # Background asyncio tasks: run_generation (chat, DB flush, titles, optimistic streaming + intent racing + tool loop) + run_assist_generation (lightweight, no DB); _drain_queue() async generator helper
-│       │   ├── intent.py          # Intent routing: classify_intent() makes fast non-streaming LLM call to detect tool intent before streaming
+│       │   ├── generation_task.py   # Background asyncio tasks: run_generation (chat, DB flush, titles, intent-first pipeline + tool loop) + run_assist_generation (lightweight, no DB)
+│       │   ├── intent.py          # Intent routing: classify_intent() makes fast non-streaming LLM call; IntentResult has ack field (one-sentence acknowledgment streamed as TTFT)
 │       │   ├── tools.py            # LLM tool definitions (create/delete note+task, update_note w/tag management, get_note, list_notes, search_notes w/type filter, list_tasks, full CalDAV suite incl. search_todos) + execute_tool dispatcher
 │       │   ├── tag_suggestions.py # LLM-powered tag suggestions: suggest_tags() builds prompt with existing tags, calls generate_completion, parses JSON response
 │       │   ├── caldav.py          # CalDAV integration: full event lifecycle (create/list/search/update/delete), todos (create/list/search/update/complete/delete), list_calendars, timezone (ZoneInfo), reminders (VALARM), attendees, multi-calendar search
@@ -317,7 +321,7 @@ fabledassistant/
     │   │   ├── auth.ts          # Auth state: user, isAuthenticated, isAdmin, oauthEnabled, localAuthEnabled, login/register/logout/checkAuth/checkHasUsers
     │   │   ├── notes.ts         # CRUD + tag filter, resolveTitle, convertToTask, convertToNote, fetchBacklinks, fetchAllTags (with toast errors)
     │   │   ├── tasks.ts         # CRUD + status/priority filter, patchStatus (with toast errors)
-    │   │   ├── chat.ts          # Conversation CRUD, sendMessage (SSE streaming), status polling (memory-leak-safe _pollUntilLoaded), running models, model warming, updateConversationModel (with toast errors)
+    │   │   ├── chat.ts          # Conversation CRUD, sendMessage (SSE streaming, includeNoteIds param), status polling (memory-leak-safe _pollUntilLoaded), running models, model warming, updateConversationModel (with toast errors)
     │   │   ├── settings.ts      # App settings: assistantName, defaultModel, installedModels, defaultChatModel, defaultIntentModel, pullModel, deleteModel (with toast errors)
     │   │   └── toast.ts         # Toast notification state (success/error/warning), 4s auto-dismiss, dismiss(id)
     │   ├── types/
@@ -342,7 +346,7 @@ fabledassistant/
     │   │   ├── RegisterView.vue # Register form with password confirmation; shows "closed" message when registration disabled
     │   │   ├── RegisterInviteView.vue # Invitation-based registration: validates token, creates account with pre-set email
     │   │   ├── UserManagementView.vue # Admin user management: registration toggle, invitations (send/revoke), user list with delete
-    │   │   ├── ChatView.vue     # Dedicated /chat page: responsive sidebar (overlay on mobile), bubble messages, note picker, persistent context sidebar (right panel, hidden mobile), model selector in header
+    │   │   ├── ChatView.vue     # Dedicated /chat page: responsive sidebar (overlay on mobile), bubble messages, note picker, context sidebar with “In Context” (user-included, ×) + “Suggested” (auto-found, +), model selector in header
     │   │   ├── HomeView.vue     # Chat-first dashboard: quick actions + chat widget (top, full-width), inline response panel, two-column grid (3fr tasks / 2fr notes); task sections: Overdue, Due Today, Due This Week, High Priority, In Progress, Other (capped 10, due-dated first); 8 recent notes; model warming on mount
     │   │   ├── SettingsView.vue # Settings page: assistant name, chat/intent model dropdowns (populated from installed models), email change (with password confirmation for local-auth users), change password, notifications, CalDAV, SMTP (admin), base URL (admin), data export/restore (admin)
     │   │   ├── NotesListView.vue # Note list: search, sort, tag filter pills, pagination
@@ -354,7 +358,7 @@ fabledassistant/
     │   ├── components/
     │   │   ├── LogsView.vue     # Admin log viewer: stats summary, category/search/date filters, paginated table with IP column + expandable detail rows (expands on ip_address or details)
     │   │   ├── AppHeader.vue    # Nav bar: brand, nav links (incl. admin Logs), status indicator, theme toggle, user info + logout, hamburger menu (mobile)
-    │   │   ├── ChatPanel.vue    # Slide-out chat panel (right side overlay, receives contextNoteId prop), bubble-style messages, floating dark input, note picker, context pills with promote/exclude
+    │   │   ├── ChatPanel.vue    # Slide-out chat panel (right side overlay, receives contextNoteId prop), bubble-style messages, floating dark input, note picker, context pills with promote (+) only (no exclude)
     │   │   ├── ModelSelector.vue # Model dropdown (v-model pattern): fetches installed + running models, hot/cold indicators
     │   │   ├── DashboardChatInput.vue # Inline chat bar: ModelSelector + note picker + textarea + send button; emits submit event
     │   │   ├── ToolCallCard.vue # Compact card for tool call results (created/deleted task/note, note content, notes list, search results, CalDAV events/todos, errors) + suggested tag pills with apply-on-click
@@ -432,7 +436,7 @@ fabledassistant/
 | GET | `/api/chat/conversations/:id` | Get conversation with all messages |
 | DELETE | `/api/chat/conversations/:id` | Delete conversation (cascades to messages) |
 | PATCH | `/api/chat/conversations/:id` | Update conversation title or model (body: `{title?, model?}`) |
-| POST | `/api/chat/conversations/:id/messages` | Start generation: save user message, launch background task, return 202 (body: `{content, context_note_id?, exclude_note_ids?}`) |
+| POST | `/api/chat/conversations/:id/messages` | Start generation: save user message, launch background task, return 202 (body: `{content, context_note_id?, include_note_ids?}`) |
 | GET | `/api/chat/conversations/:id/generation/stream` | SSE endpoint tailing generation buffer; supports `Last-Event-ID` reconnection; emits `context`, `chunk`, `done`, `error` events |
 | POST | `/api/chat/conversations/:id/generation/cancel` | Cancel active generation (sets cancel_event, saves partial content) |
 | POST | `/api/chat/messages/:id/save-as-note` | Save assistant message as a new note (LLM-generated title, tagged `chat`) |
@@ -565,9 +569,12 @@ When adding a new migration, follow these conventions:
 - Background generation with `GenerationBuffer` (in-memory SSE fan-out, `Last-Event-ID` reconnect, 60s cleanup)
 - Stop generation with partial content preservation
 - Note-aware context building: current note + keyword search for related notes + URL fetching
-- **Persistent context sidebar:** Right panel in ChatView accumulates auto-found notes across turns.
-  Manually attached note appears with 📌 pin, clears after send. × excludes from future auto-search.
-  Hidden on mobile (≤768px). Replaces old ephemeral context pills in the message stream.
+- **Context sidebar (Phase 21):** Right panel in ChatView shows two sections: **Suggested** (auto-found
+  by semantic/keyword search — click `+` to include) and **In Context** (explicitly included by user —
+  click `×` to remove). Removing an included note moves it back to Suggested. Manually attached note
+  appears with 📌 pin in the In Context section, clears after send. Hidden on mobile (≤768px).
+  Auto-found notes are shown as sidebar candidates only — they are NOT injected into the system prompt
+  automatically, keeping the system prompt prefix stable for Ollama KV cache reuse.
 - Note picker (paperclip) in chat input; attached note title passed to store for optimistic render.
   `context_note_title` synthesised server-side at conversation load via batch `get_notes_by_ids()`;
   message badge shows note title instead of "Note #N".
@@ -614,21 +621,22 @@ When adding a new migration, follow these conventions:
     (`search_todos` keyword-filters the todo list — companion to `list_todos`)
 - **Streaming status transparency:** The backend emits `status` SSE events at each pipeline stage
   so the user always sees what's happening instead of a blank progress dot. Stages:
-  (1) `"Generating response..."` immediately after context is built (no blocking wait for intent);
-  (2) human-readable tool label (e.g. `"Creating calendar event..."`) before each tool executes
-  (both intent-routed and native); (3) `"Composing response..."` before tool follow-up rounds.
+  (1) intent ack text streamed as a `chunk` event (tool responses — TTFT ~400ms) or
+  `"Generating response..."` status event (chat-only responses);
+  (2) human-readable tool label (e.g. `"Creating calendar event..."`) before each tool executes;
+  (3) `"Composing response..."` before tool follow-up rounds.
   Frontend: `chat.ts` stores `streamingStatus` ref, cleared on first content chunk or on done/error.
   `ChatView.vue` shows a pulsing dot + italic label above the content while status is active, then
   falls back to the blinking cursor when content streams in. `HomeView.vue` dashboard panel shows
   the status label in place of `...` before any content arrives.
-- **Intent routing (optimistic streaming):** On the first round, the backend races intent
-  classification against the start of the LLM stream using `asyncio.wait(FIRST_COMPLETED)`.
-  The LLM stream is immediately started into an `asyncio.Queue` while `classify_intent()` runs
-  concurrently. If the stream produces its first token before classification completes, the intent
-  task is cancelled and the user sees tokens immediately (zero blocking for pure chat). If
-  classification finishes first and detects a tool call, the stream is cancelled and the tool
-  executes directly — bypassing the model's native (sometimes unreliable) tool calling API. Falls
-  through to normal streaming when no tool is detected or classification fails.
+- **Intent routing (intent-first pipeline, Phase 21):** On the first round, `build_context()` and
+  `classify_intent()` run concurrently. Once intent returns (~400ms), the pipeline immediately acts:
+  if a tool is detected, the intent's one-sentence `ack` field is streamed as the first chunk
+  (becoming TTFT), the tool executes, then the main model generates a follow-up response with the
+  tool result. For chat-only responses, the model streams directly with no ack prefix.
+  No optimistic streaming queue or race — eliminates wasted GPU prefill when intent won the race.
+  `IntentResult.ack` (one-sentence acknowledgment) is embedded in the intent JSON output, so no
+  additional LLM call is needed for acknowledgment. Intent model `max_tokens` 350 (was 200).
   Dedicated intent model configurable via `OLLAMA_INTENT_MODEL` env var (default `qwen2.5:1.5b`)
   or per-user `intent_model` setting — smaller/faster model for routing. Main model default
   is `qwen3:latest` (configurable via `OLLAMA_MODEL` env var or per-user `default_model` setting
@@ -641,6 +649,8 @@ When adding a new migration, follow these conventions:
   search_events), update_note vs create_note disambiguation, reminder_minutes conversion,
   delete_note vs delete_task disambiguation, get_note for "read/show me this note", list_notes for
   "browse/list notes", tag management via update_note (tag_mode add/remove), search_todos.
+  `generate_completion` (used by intent classifier) retries on HTTP 500 (3 attempts, 3s/6s delays)
+  to handle cold model loading without failing intent classification.
 - **CalDAV calendar integration:** Per-user CalDAV settings (URL, username, password, calendar name, timezone).
   LLM tools: `create_event` (all_day, recurrence, timezone, reminder_minutes, attendees, calendar_name),
   `list_events`, `search_events`, `update_event`, `delete_event`, `list_calendars`,