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Duration 14 hours
Course Outline
Introduction to Prompt Engineering with Ollama
- Analyzing Ollama’s functional capabilities and constraints
- Core principles of effective prompt engineering
- Examining the dynamics between prompts and responses
Priming and Instruction Architecture
- Establishing role-based instructional frameworks
- Refining initial prompts to achieve task-specific results
- Reviewing case studies of successful priming strategies
Chain-of-Thought and Reasoning Strategies
- Facilitating step-by-step logical reasoning
- Structuring coherent logical flows within prompts
- Striking a balance between verbosity and precision
Prompt Templates and Reusability
- Developing reusable prompt frameworks
- Implementing dynamic context insertion
- Scaling prompt engineering efforts through template usage
Context Window Management
- Optimizing interactions within limited context windows
- Applying summarization and context reduction techniques
- Utilizing sliding window and memory-based methods
Multi-Stage Prompting Architecture
- Linking prompts to address complex tasks
- Constructing pipelines that utilize intermediate outputs
- Implementing iterative refinement and feedback loops
Evaluation and Optimization
- Establishing success metrics for prompt performance
- Conducting systematic A/B testing of prompt strategies
- Pursuing continuous improvement in prompting methodologies
Summary and Future Directions
Requirements
- Foundational knowledge of large language models
- Proficiency in Python programming
- Familiarity with prompt-based interaction methods
Target Audience
- Prompt engineers
- Software developers
- Product managers exploring Ollama capabilities
