Course info

In the ever-evolving field of artificial intelligence, mastering the nuances of prompt engineering is essential for professionals aiming to harness the full potential of generative AI. This 2-month course on "Advanced Techniques in Prompt Engineering" is meticulously designed for engineers who are keen to deepen their expertise and apply advanced prompt engineering techniques in an enterprise setting.

This course emphasizes a hands-on approach, with extensive lab exercises, practical projects, quizzes, and case studies. Participants will gain practical experience in building generative AI applications and will engage with cutting-edge research papers to stay abreast of recent advancements in the field.

Course Topics

The following key topics will be covered:

• LLM Inference Endpoints and Frameworks: Understanding the infrastructure and frameworks supporting large language models.
• Overview of Prompting: Exploring various types of prompting, including text, visual, audio, and video.
• Prompting as Control Flow: Techniques for using prompts to control the flow of AI operations.
• Limitations of LLMs and Manual Prompting: Identifying and addressing the constraints of large language models.
• COSTAR and Other Prompting Techniques: Advanced techniques for effective prompting.
• Case Studies & Domain Applications: Practical examples and applications in various domains.
• Prompting in Structured Output Generation: Methods for generating structured outputs through prompts.
• Function Call-based Prompting: Instructor: Using function calls to guide prompting.
• Constrained Sampling: Outlines: Techniques for constrained sampling to improve prompt outcomes.
• Constrained Sampling: Microsoft Guidance: Insights from Microsoft's approaches to constrained sampling.
• Programmatic Prompting and Automation: Automating prompt generation and optimization.
• Programmatic Prompting: DSPy: Leveraging DSPy for programmatic prompting.
• Programmatic Prompting: SAMMO: Using SAMMO for advanced prompt automation.
• Prompt Program Demo: STORM: Demonstrations of prompt programs using STORM.
• Prompt Monitoring: Techniques for observing and monitoring prompt performance.
• LLM Observability: Arize Phoenix: Monitoring large language models with Arize Phoenix.
• LLM Observability: LogFire: Using LogFire for prompt observability.
• LLM Observability: LangFuse: LangFuse for comprehensive LLM monitoring.
• Prompt Security: Ensuring the security and integrity of prompt engineering processes.
• Synthetic Data Generation: Creating synthetic data for robust prompt development.
• TextGrad: Advanced techniques for secure prompt engineering.

By the end of this course, participants will possess a comprehensive understanding of advanced prompt engineering techniques and will be able to apply these skills to develop and optimize generative AI applications in an enterprise context. Join us to enhance your proficiency and remain at the cutting edge of AI technology.