Developing robust and effective Generative AI applications involves several key stages, from initial prompt engineering to integrating advanced functionalities and ensuring continuous improvement.
Prompt Engineering & Customization
Prompt engineering is both an "art and science" critical for optimizing the output of generative AI models.
- Fundamentals: Involves constructing, iterating on, and validating prompts to improve response quality. Key concepts include understanding "prompt, temperature, and tokens."
- Advanced Techniques: Applying prompt engineering to "improve the outcome of your prompts" and "configure your prompts to vary the output."
- Fine-Tuning: A method to "improve the performance of your generative AI models" by further training pre-trained language models on specific datasets. This addresses benefits, challenges, and limitations, and helps determine "When, and why, is fine tuning useful?"
Integration and Advanced Features
Generative AI applications can be significantly enhanced by integrating external functionalities and data.
- Function Calling: Enables LLMs to interact with external applications by making "function calls." This expands the capabilities of LLMs beyond text generation.
- Low-Code AI Applications: Platforms like Microsoft Power Platform, leveraging Generative AI, Copilot, and AI Builder, empower both developers and non-developers to build AI applications and flows with minimal coding.
- Retrieval Augmented Generation (RAG): A technique for "grounding your data in your LLM application," integrating external knowledge into LLMs to enhance their responses. This involves "storing data, including both embeddings and text," often utilizing vector databases.
- Vector Databases: Used to store "numerical representations of data also known as vectors" (embeddings) for efficient semantic search and RAG implementation.