Advanced Techniques in Prompt Engineering

Are you looking to enhance your prompt engineering skills for Azure OpenAI GPT models? Large Language Models This blog will walk you through various advanced techniques to improve the accuracy and grounding of responses generated by Large Language Models (LLMs). Before diving in, make sure you're familiar with the basics of prompt engineering and the two APIs that play a crucial role in prompt design: Chat Completion API and Completion API.

System Message

System messages set the context for your AI assistant, defining its personality and providing instructions for how it should respond. Examples of system messages include:

• "Assistant is a large language model trained by OpenAI."

• "Assistant is an intelligent chatbot designed to help users answer technical questions about Azure OpenAI Service."

Bear in mind that crafting a well-designed system message doesn't guarantee a specific outcome, but it can increase the likelihood of desired results.

Few-Shot Learning

Few-shot learning involves providing training examples as part of the prompt to give additional context to the model. With the Chat Completions API, a series of User and Assistant messages can serve as examples for few-shot learning.

Non-Chat Scenarios

While the Chat Completion API is optimized for multi-turn conversations, it can also be used for non-chat scenarios.

Prompt Engineering Techniques

1. Start with clear instructions: Place the task description at the beginning of the prompt before adding additional context or examples.

2. Repeat instructions at the end: To mitigate recency bias, experiment with repeating instructions at the end of the prompt.

3. Prime the output: Include specific words or phrases at the end of the prompt to obtain a model response that follows the desired format.

4. Add clear syntax: Use punctuation, headings, and section markers to communicate intent and make outputs easier to parse.

5. Break the task down: LLMs often perform better when tasks are divided into smaller steps.

6. Use of affordances: Utilize affordances to help reduce fabricated answers and obtain up-to-date information.

7. Chain of thought prompting: Instruct the model to proceed step-by-step and present all steps involved, which reduces the likelihood of inaccurate outcomes.

8. Specify the output structure: Requesting citations or a specific structure can help generate more grounded and accurate responses.

9. Temperature and Top_p parameters: Adjust these parameters to control the randomness of the model response.

10. Provide grounding context: Offer reliable data for the model to draw its responses, especially when up-to-date information is required.

By leveraging these advanced techniques, you can increase the accuracy and grounding of responses generated by LLMs like GPT-4. Remember to always validate the generated responses, as even a carefully crafted prompt may not generalize broadly to certain use cases.