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Duration 14 hours
Course Outline
Introduction to Prompt Engineering with Ollama
- Grasping Ollama’s core capabilities and inherent limitations
- Core principles of prompt engineering
- Analyzing the dynamics between prompts and responses
Priming and Instruction Design
- Establishing role-based instructional directives
- Refining initial prompts to achieve task-specific results
- Examining case studies of successful priming strategies
Chain-of-Thought and Reasoning Prompts
- Fostering step-by-step logical reasoning
- Structuring coherent and logical flow
- Striking a balance between detail and precision
Prompt Templates and Reusability
- Creating reusable prompt frameworks
- Implementing dynamic context insertion
- Scaling prompt engineering efforts through templates
Context Window Strategies
- Optimizing limited context window usage
- Utilizing summarization and context reduction methods
- Applying sliding window and memory-based techniques
Multi-Stage Prompting
- Linking prompts to handle complex tasks
- Developing pipelines that utilize intermediate outputs
- Employing iterative refinement and feedback mechanisms
Evaluation and Optimization
- Establishing clear success metrics for prompts
- Conducting systematic A/B testing of prompt strategies
- Pursuing continuous improvement in prompting methods
Summary and Next Steps
Requirements
- A foundational understanding of large language models
- Proficiency in Python programming
- Familiarity with prompt-based interaction patterns
Target Audience
- Prompt engineers
- Software developers
- Product managers exploring Ollama capabilities
