Notion AI is convenient but it sends every piece of text you ask it to process to Anthropic’s or OpenAI’s servers. For personal notes, confidential work documents, client data, or anything sensitive, that’s a meaningful privacy trade-off. With Ollama and a few tools, you can replicate most of what Notion AI does — writing assistance, summarisation, content generation — using a local model that never transmits your content anywhere. This guide covers the most practical approaches for getting AI writing assistance on your notes and documents without cloud APIs.
What Notion AI Actually Does
Notion AI wraps standard LLM capabilities in a clean interface: summarise a page, improve writing, continue writing from where you left off, generate a first draft from a prompt, translate text, fix grammar and spelling, and answer questions based on page content. All of these are things any capable local LLM can do — the difference is the integration depth (Notion AI sits inside your editor) and the convenience (one click). The Ollama alternative requires more setup but provides better privacy and zero marginal cost.
Option 1: Open WebUI as Your Writing Environment
The simplest approach: use Open WebUI (backed by Ollama) as a parallel workspace alongside Notion. Write your rough notes or content in Notion as usual, then paste sections into Open WebUI when you need AI assistance — improving clarity, summarising, generating alternatives, expanding bullet points. This is low-friction to set up (Open WebUI is one Docker command) and works for most common Notion AI use cases. The workflow is slightly less integrated than Notion AI but the privacy guarantee is complete and the quality from a 7B+ model is comparable or better for most writing tasks.
docker run -d -p 3000:8080 \
--add-host=host.docker.internal:host-gateway \
-v open-webui:/app/backend/data \
ghcr.io/open-webui/open-webui:mainAccess at http://localhost:3000. Create a custom system prompt for writing assistance: “You are a skilled writing assistant. Help improve clarity, conciseness, and flow. When asked to improve text, return the improved version directly without commentary.” This system prompt plus a 7B model gives you a capable private writing assistant.
Option 2: Logseq + Ollama for Note-Based Writing
If you are willing to switch note-taking apps, Logseq with the GPT3 Ollama plugin gives you in-editor AI assistance similar to what Notion AI provides. Logseq’s block-based structure maps well to AI-assisted workflows: select the blocks you want to work with, trigger an AI command, and the result inserts below. The key difference from Notion AI is that the AI actions are explicit commands you trigger rather than inline suggestions, which some users prefer for the clarity it provides about when AI is involved. For a detailed Logseq + Ollama setup guide, the previous article in this series covers the full configuration.
Option 3: A Custom Writing Assistant App
For the most Notion AI-like experience in a standalone tool, build a small Streamlit app that wraps Ollama with the specific prompts you use most. This takes about an hour and produces a tool tailored to your exact needs. A starter template:
import streamlit as st
import ollama
st.set_page_config(page_title="Private Writing Assistant", layout="wide")
st.title("Writing Assistant — Powered by Ollama")
MODEL = "llama3.1"
ACTIONS = {
"Improve writing": "Improve the clarity, flow, and conciseness of the following text. Return only the improved version:",
"Summarise": "Summarise the following text in 3-5 bullet points:",
"Expand": "Expand the following notes into well-written paragraphs:",
"Fix grammar": "Fix grammar and spelling errors. Return only the corrected text:",
"Make shorter": "Make this text more concise while preserving all key information:",
"Generate outline": "Generate a structured outline for the following topic or content:",
}
col1, col2 = st.columns([1, 1])
with col1:
action = st.selectbox("Action", list(ACTIONS.keys()))
input_text = st.text_area("Your text", height=300, placeholder="Paste your text here...")
if st.button("Process", type="primary") and input_text.strip():
prompt = f"{ACTIONS[action]}\n\n{input_text}"
with col2:
st.subheader("Result")
placeholder = st.empty()
result = ""
for chunk in ollama.chat(
model=MODEL,
messages=[{"role": "user", "content": prompt}],
stream=True
):
result += chunk["message"]["content"]
placeholder.markdown(result)
with col2:
if "result" in dir() and result:
st.download_button("Copy to clipboard", result, file_name="result.txt")
Run with streamlit run writing_assistant.py. This gives you a clean two-panel interface — input on the left, AI-processed output on the right — with the most common Notion AI actions available as a dropdown. Add or remove actions from the ACTIONS dictionary to match your workflow.
Figure 1 — Notion AI vs Ollama Alternatives: Feature Comparison
Which Local Models Work Best for Writing Tasks
Writing assistance tasks — improving clarity, fixing grammar, expanding notes, generating structured content — benefit from models with strong instruction following and good English language quality. The best choices from the Ollama library for writing work: Llama 3.1 8B is excellent for general writing assistance and the go-to recommendation — strong instruction following, clean prose output, and fast enough for interactive use. Mistral Small 3 (24B) produces noticeably better prose quality on nuanced editing tasks if your hardware supports it. Phi-4 (14B) is particularly strong for tasks requiring precise, structured output — generating formal outlines, writing in specific styles, producing content that needs to follow particular formats. For casual note-taking and quick summarisation, Llama 3.2 3B generates fast enough to feel instant and handles straightforward writing tasks adequately. The choice depends on your hardware and the complexity of writing tasks you do most often — start with Llama 3.1 8B and upgrade if you find the outputs need more sophistication.
Prompting for Writing Quality
The difference between mediocre and excellent AI writing assistance often comes down to prompt specificity. Generic prompts produce generic outputs. Specific prompts produce specific, useful outputs. Instead of “improve this text,” try “improve this text for a technical blog audience — make it more direct and concrete, cut any padding, and ensure each sentence adds something new.” Instead of “summarise,” try “summarise these meeting notes into: (1) decisions made, (2) action items with owners, (3) open questions requiring follow-up.” The more precisely you specify what kind of output you want, the more useful the result. This prompt precision is actually easier with Ollama than with Notion AI because you have full control over the system prompt — you can define the model’s persona, output format expectations, and quality standards once in the system prompt rather than re-specifying them in every query.
Storing and Reusing Your Best Prompts
The prompts that work well for your specific writing style and use cases are worth keeping. In Open WebUI, save effective system prompts as named presets and switch between them. In the custom Streamlit app, add them to the ACTIONS dictionary. In Logseq, create a page called “AI Prompts” and keep your most effective prompts there as blocks — you can copy-paste them into AI queries when needed. Over time, your collection of tested, effective prompts becomes one of the most valuable assets in your local AI writing setup — more valuable than the specific model, because good prompts reliably produce good outputs across model versions and upgrades, while the models themselves will change. The investment in refining prompts compounds: each improvement you make benefits every future use of that prompt.
The Privacy Case in Plain Terms
Notion AI processes your text on servers owned by Notion (and underlying model providers). If you write personal journal entries, medical notes, financial plans, client project details, or confidential work documents in Notion, enabling Notion AI means those documents get processed by third-party AI services under their privacy policies. With the Ollama setup described here, your text never leaves your machine. The AI processing happens locally — your drafts, your revisions, your private notes stay private. For users who are careful about what they share digitally, this is the fundamental argument for local AI tools over cloud AI writing assistants. The trade-off is convenience and integration depth — Notion AI is more seamlessly integrated into the Notion editor than any local alternative. But if you are already choosing Notion for its local-first data model rather than fully cloud-dependent alternatives, extending that same philosophy to AI assistance is the natural next step.
Comparing Quality: Notion AI vs Local Models
An honest comparison: Notion AI, backed by Claude or GPT-4, produces higher quality outputs than a 7B local model on complex writing tasks. This is simply true — frontier models are more capable than what most consumer hardware can run locally. The question is whether the quality gap matters for your specific use cases. For the most common Notion AI use cases — fixing grammar, summarising a page of notes, expanding a bullet list into paragraphs — a good 7B model like Llama 3.1 8B or Qwen 2.5 7B produces outputs that are genuinely useful and often hard to distinguish from Notion AI outputs when the task is well-defined and the prompt is specific. The quality gap is more apparent on open-ended creative tasks, complex reasoning, and nuanced editorial work. If your use case is primarily these more demanding tasks, you may find the local model underwhelming. If your use case is the common, more structured writing assistance tasks, local models are adequate and the privacy and cost advantages are compelling. The practical approach: try both with your actual notes on your actual tasks and make the decision based on observed quality rather than benchmark numbers.
Multi-Step Writing Workflows
Where local AI writing assistance really shows its value is in multi-step workflows where you iterate on content through several AI-assisted passes. With cloud APIs, each iteration costs money and sends your evolving content to external servers repeatedly. With Ollama, you can iterate freely — run the improve-writing action on a paragraph five times, trying different approaches, without any additional cost or privacy concern. A useful multi-step pattern for polishing rough notes into publishable content: first pass asking the AI to structure and organise the raw ideas into a logical flow, second pass asking it to expand each section with more depth and specific examples, third pass asking it to tighten the language and cut repetition, fourth pass asking it to check for consistency and suggest a stronger conclusion. Each pass is a separate Ollama call, and the total cost is zero beyond the electricity to run your GPU. This iterative approach, which would cost several dollars per article with Notion AI or OpenAI, is effectively free with a local model — which changes how freely you can experiment with your writing workflow.