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Prompt Engineering - Best Practice for Productive Customer | Maximilian Vogel
Learn best practices for productive customer interactions through prompt engineering techniques, including automated optimization, chain of thought, and more, in this engaging conference talk by Maximilian Vogel.
- Automated prompt engineering: Use deterministic processes to optimize prompts, such as filtering, sorting, and rewriting content.
- Chain of thought: Create a structured prompt that guides the model through a step-by-step process, making it easier to understand and respond to complex questions.
- Contextualization: Incorporate context data from retrieval processes to help the model understand the conversation history and respond accordingly.
- Edge case handling: Define how the model should handle off-topic questions, harmful user inputs, and other edge cases to prevent incorrect responses.
- Format specification: Clearly specify the output format, length, and level of detail to ensure the model generates accurate and relevant responses.
- Hypernymy: Use hierarchical taxonomies to define relationships between concepts and entities, making it easier for the model to understand and respond to questions.
- Interactivity: Design prompts that encourage interactive dialogue, such as asking follow-up questions or providing additional context.
- JSON output: Use JSON format to specify the output structure and make it easier for the model to generate accurate responses.
- Linguistic processing: Pre-process user input to remove noise, extract relevant information, and improve the model’s ability to understand and respond to questions.
- Multimodal input: Allow users to input text, images, or other multimedia to enable more natural and interactive dialogue.
- Off-topic handling: Define how the model should respond to off-topic questions, such as ignoring them or providing a correction.
- Output evaluation: Use evaluation prompts to assess the model’s performance and accuracy, and adjust the prompts accordingly.
- Prompt engineering: Use a combination of technical and linguistic expertise to design and optimize prompts for specific use cases and applications.
- Query optimization: Optimize prompts for specific queries or tasks, such as using natural language processing techniques to improve response accuracy.
- Semantic search: Use semantic search techniques to retrieve relevant information from large datasets and improve the model’s ability to understand and respond to questions.
- Syntax analysis: Analyze the syntax and structure of user input to improve the model’s ability to understand and respond to questions.
- Tokenization: Use tokenization techniques to break down user input into individual tokens and improve the model’s ability to understand and respond to questions.