1. Identifying the AM Task
The first step is defining the problem scope: recognizing challenges in AM that could benefit from AI-driven solutions, such as design optimization, process parameter tuning, or defect detection.
Benchmatking Metrics2. Selecting a Base Model
Choosing the right AI model is essential. Options include Large Language Models (LLMs), diffusion models, and specialized tools like GPT-4o and DALL·E, each with different capabilities for AM applications.
Benchmarking Tools3. Assessing the Base Model
Evaluating the selected model's initial performance through zero-shot prompting before considering more advanced approaches like fine-tuning.
4. Prompt Engineering
If zero-shot performance is insufficient, advanced prompt techniques like few-shot prompting, Chain-of-Thought (CoT), and Retrieval-Augmented Generation (RAG) are explored.
Understanding Prompt Engineering5. Fine-Tuning
When prompt engineering is insufficient, model fine-tuning with AM-specific datasets is required for improved accuracy and domain adaptation.
Understanding fine-tune ApproachReady to Explore Further?
Continue your journey by exploring specific aspects of GenAI in AM: