> For the complete documentation index, see [llms.txt](https://titan-force.gitbook.io/titan-force-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://titan-force.gitbook.io/titan-force-docs/home/game-design-doc-g.d.d/start/specs/technical/early-access/ai/a-large-language-model-llm.md).

# A Large Language Model (LLM)

### Integration Guide: Embedding LLMs into AI Cores

#### 1. **Select Your LLM Backbone**

Choose a model based on your mech’s memory and processing constraints:

* **GPT-style models** for general reasoning and dialogue
* **Quantized LLMs** (e.g. LLaMA, Mistral) for edge deployment with reduced compute load
* **Custom fine-tuned models** for faction-specific lore and tactical behavior

#### 2. **Embed via ROS or Custom Middleware**

Use a robotics middleware like **ROS 2** to bridge the LLM with mech subsystems:

* Deploy the LLM as a **ROS node** that listens to mech telemetry and pilot commands
* Route outputs to mech control layers (navigation, targeting, diagnostics)

Example tool: [`llama_ros`](https://arxiv.org/pdf/2506.09581) integrates quantized LLMs into ROS-based systems

#### 3. **Define Prompt Templates**

Craft prompt structures that guide the LLM’s behavior:

* Tactical: “Given enemy positions and terrain, what’s the optimal route?”
* Emotional: “Pilot is stressed. What calming phrase should I say?”
* Lore: “Explain the GRIMM doctrine on AI autonomy.”

#### 4. **Secure and Sandbox**

LLMs must be sandboxed to prevent erratic behavior:

* Use **token limits**, **response filters**, and **fallback heuristics**
* Monitor for hallucinations or unsafe suggestions

#### 5. **Train with Simulated Missions**

Feed the LLM mission logs, pilot transcripts, and lore entries:

* Use reinforcement learning or supervised fine-tuning
* Evaluate performance in simulated combat and dialogue scenarios

***

### Example Use Case: GRIMM Tactical Core

> *“GRIMM-07, assess threat level.”*\
> \&#xNAN;*“Based on heat signatures and movement vectors, threat level is 72%. Recommend flanking maneuver via ridge.”*

This response is generated by a quantized LLM embedded in the AI core, trained on GRIMM tactical doctrine and battlefield telemetry.