Prompt Engineering
Prompt engineering is used to create effective instructions for AI models to achieve desired outcomes.Teammately's platform implements advanced prompt engineering techniques optimized for different models and use cases.
Our approach uses a multi-step generation architecture that breaks complex tasks into smaller components:
- Reasoning: Analytical and logical processing of information
- Thinking: Evaluating options and considering multiple perspectives
- Formatting: Structuring output in the most appropriate way
- User Input Expansion: Enriching sparse user queries with relevant context
Prompt Components​
Effective prompts typically contain several key components:
- System Instructions: Define the AI's role, capabilities, and operational guidelines. These set the foundation for how the model should approach the task
- Context Setting: Provide relevant background information and establish the framework within which the model should operate
- Input Formatting: Structure user inputs consistently to help the model parse and understand them properly
- Output Structuring: Specify how responses should be formatted, whether as natural text, JSON, or other structured formats
- Chain-of-Thought Prompting: Guide models through explicit reasoning steps to improve accuracy on complex problems
- Self-Refinement: Enable models to generate initial responses, then critically review and improve them
Prompt Optimization Process​
Teammately's system continuously refines prompts through:
- Testing: Evaluating prompts against diverse test cases
- Analysis: Identifying failure modes and performance patterns
- Refinement: Iteratively improving instructions and structure
- Parameter Alignment: Matching prompts with optimal parameter settings
Our playground environment allows for easy testing and iteration of prompt engineering before deployment.