Productionizing Prompt Engineering

Challenges of Productionizing Prompt Engineering

• Prompt engineering involves writing instructions in natural language, which is more flexible than programming languages.
• User-defined prompts can lead to silent failures, while ambiguity in language models' generated responses can cause inconsistent output formats and user experiences.
• OpenAI is actively working to mitigate ambiguity, but it can be mitigated by applying engineering rigor.
• A common technique for prompt engineering is to provide a few examples in the prompt and evaluate the language model's understanding and overfitting.
• Prompt versioning is crucial for tracking the performance of each prompt and making small changes to avoid very different results.
• Prompt optimization can be achieved through techniques like Chain-of-Thought, generating multiple outputs, and breaking big prompts into smaller ones.
• Auto-optimization tools are available but can be expensive and often just apply these same prompt optimization techniques.
• The more explicit detail and examples in the prompt, the better the model performance, but it also increases inference cost.
• OpenAI API charges for both input and output tokens, with a simple prompt ranging from 300-1000 tokens and more context adding up to 10k tokens.
• Inconsistent output formats can be solved by crafting prompts to be explicit about the output format, but there is no guarantee that the outputs will always follow this format.
• Stochastic LLMs can be forced to give the same response by setting temperature = 0, but it does not inspire trust in the system.
• When doing fewshot learning, it is essential to evaluate whether the LLM understands the examples given in the prompt and whether it overfits to these fewshot examples.

Challenges of Productionizing Prompt Engineering

• Prompt engineering involves writing instructions in natural language, which is more flexible than programming languages.
• User-defined prompts can lead to silent failures, while ambiguity in language models' generated responses can cause inconsistent output formats and user experiences.
• OpenAI is actively working to mitigate ambiguity, but it can be mitigated by applying engineering rigor.
• A common technique for prompt engineering is to provide a few examples in the prompt and evaluate the language model's understanding and overfitting.
• Prompt versioning is crucial for tracking the performance of each prompt and making small changes to avoid very different results.
• Prompt optimization can be achieved through techniques like Chain-of-Thought, generating multiple outputs, and breaking big prompts into smaller ones.
• Auto-optimization tools are available but can be expensive and often just apply these same prompt optimization techniques.
• The more explicit detail and examples in the prompt, the better the model performance, but it also increases inference cost.
• OpenAI API charges for both input and output tokens, with a simple prompt ranging from 300-1000 tokens and more context adding up to 10k tokens.
• Inconsistent output formats can be solved by crafting prompts to be explicit about the output format, but there is no guarantee that the outputs will always follow this format.
• Stochastic LLMs can be forced to give the same response by setting temperature = 0, but it does not inspire trust in the system.
• When doing fewshot learning, it is essential to evaluate whether the LLM understands the examples given in the prompt and whether it overfits to these fewshot examples.