**Summary of the Document:**

This paper introduces a framework for leveraging **Large Language Models (LLMs)** in **manufacturing** to enhance data integration and decision-making in **Industry 4.0** environments. The framework, called **FILLIS (Factory Integrated Logic and Language Interface System)**, demonstrates how LLMs can streamline data processing, provide insights, and assist in tasks like machine operation explanations and language translation.

### **Key Points:**
1. **Challenges in Manufacturing Data:**  
   - The rise of **IoT** and **Big Data** in manufacturing has created vast amounts of data, but interpreting and correlating this data requires specialized expertise.
   - **AI and LLMs** can help process and analyze this data efficiently.

2. **LLM Applications in Manufacturing:**  
   - LLMs can assist in **knowledge retrieval, documentation analysis, and real-time decision-making**.
   - They bridge gaps in **specialized expertise** by providing accessible insights from technical documents.

3. **Proposed Framework:**  
   - **Embeddings & Vector Databases:** Convert text data into numerical representations for efficient querying.  
   - **Task Orchestrator & Conversational Agent:** Two specialized LLMs work together—one to manage tasks and external agents, the other to generate human-readable responses.  
   - **External Agents:** Used for tasks requiring calculations or additional processing beyond LLM capabilities.

4. **Case Study: FILLIS (HAAS Desktop Mill Assistant)**  
   - FILLIS was tested using a **HAAS Mill Operator’s Manual** (187 pages).  
   - It successfully provided **step-by-step startup instructions**, **G-code commands**, and **translated responses** accurately.  
   - **Limitations:** Struggled with **mathematical operations**, highlighting the need for external tools.

5. **Comparison with Traditional Methods:**  
   - FILLIS was **faster and more comprehensive** than manual searches or PDF keyword searches.  
   - It consolidated scattered information into concise, actionable responses.

6. **Future Work:**  
   - Integration of **external agents** for complex tasks.  
   - Refinement of **prompt engineering** for manufacturing-specific queries.  
   - Expansion into **more complex manufacturing applications**.

### **Conclusion:**  
LLMs like FILLIS offer **transformative potential** in manufacturing by improving **data accessibility, efficiency, and decision-making**. However, their effectiveness depends on **integration with external tools** for tasks requiring numerical or specialized processing.

**Keywords:** Large Language Models (LLMs), Manufacturing, Industry 4.0, AI, Chatbots, Natural Language Processing (NLP), IoT, Big Data.  

*(Published in Manufacturing Letters, 2024, under CC BY-NC-ND license.)*