Introduction
As AI tools become more available to everyday developers, many are realizing they don’t need cloud access, API keys, or enterprise software to benefit. A private, local setup can offer fast, flexible, and secure AI capabilities — especially when structured around a simple system: Model + Persona + Document.
This paper outlines a practical framework for integrating local AI into your workflow. It's not a tutorial (though one is linked at the end), but a conceptual guide for building your own assistant using local tools and minimal structure.
1. The Core Components
Model
The model is your language engine — a large language model (LLM) running entirely on your machine. Examples include:
You can run these models with tools like:
- Ollama — Easy model management and CLI access
- LM Studio — Local GUI frontend
- llama.cpp — For deep integration with custom workflows
Why local?
- ⚡ Fast response time
- 🔐 Full data privacy
- 💸 No API or token costs
Persona
The persona is the behavior configuration — a persistent system prompt that defines how the model should act. Think of it as the personality, role, or intent you give the assistant.
Examples:
- "You are a calm technical editor. Speak concisely and critique structure."
- "You are a smart AI co-author focused on helping the user express ideas clearly."
- "You are an Emacs expert. You answer questions precisely with minimal explanation."
Implementation:
- Store in a config file (e.g.,
~/.config/ai-profile.el) - Load into memory before every prompt session
Why use personas?
- 🧠 Shape tone and output style
- 🔄 Swap roles instantly depending on task
- 📚 Consistency across sessions
Document
The document is the content you’re working with — it provides context and acts as the primary subject of the AI’s output.
Examples:
- A blog post draft (Markdown)
- A code buffer (Python, JavaScript, etc.)
- An outline in Org-mode
- Meeting notes or a README
You inject the document into the AI's context along with the persona and a custom prompt (e.g., "Refactor this," or "Summarize the key points").
Why documents matter:
- 🧩 Give the model grounding context
- 📝 Work on real files, not abstract chat
- 🖇️ Combine with editor commands for tight integration
2. Operational Flow
The system is minimal:
Model ← Persona + Document + Prompt → Output
Basic Flow:
- Load the persona
- Extract the document content
- Append a user instruction (e.g., "Rewrite this intro")
- Send to the model via command-line or Emacs
- Insert the response back into your workspace
3. Example Use Cases
- Refactor or explain a block of code
- Improve writing tone or structure
- Convert notes to formatted documentation
- Catch inconsistencies across large text files
4. Advantages of This System
| Feature | Benefit |
|---|---|
| Local models | Fast, secure, offline-capable |
| Persona config | Consistent, swappable roles |
| Document grounding | Focused, relevant responses |
| Emacs/Spacemacs integration | Minimal interruption, keyboard-friendly |
5. Want to Try It?
Here’s a step-by-step breakdown of how I built this system into Spacemacs:
👉 I Integrated Local AI into Spacemacs – Here's How It Works
Final Thought
This isn't a magic formula. It's a simple structure that gives AI a place in your workflow without overwhelming it.
Model + Persona + Document — nothing more, nothing less.
Make it your own.
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