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Prompt Engineering

Prompt design is the process of creating a prompt that is tailored to the specific task that the system is being asked to perform.

Prompt engineering is the process of creating a prompt that is designed to improve performance.

Prompting Principles​

Principle 1: Write clear and specific instructions​

Tactic 1: Use delimiters to clearly indicate distinct parts of the input

  • Delimiters can be anything like: , """, < >, <tag> </tag>, :

Tactic 2: Ask for a structured output

  • JSON, HTML

Tactic 3: Ask the model to check whether conditions are satisfied

Tactic 4: "Few-shot" prompting

Principle 2: Give the model time to "think"​

Tactic 1: Specify the steps required to complete a task

Tactic 2: Instruct the model to work out its own solution before rushing to a conclusion

Imitating​

  • In the style of x write about y

Prompting Techniques​

prompt-techniques

Chain-of-thought​

Chain-of-thought (CoT) prompting is a technique that allows large language models (LLMs) to solve a problem as a series of intermediate steps before giving a final answer. Chain-of-thought prompting improves reasoning ability by inducing the model to answer a multi-step problem with steps of reasoning that mimic a train of thought. It allows large language models to overcome difficulties with some reasoning tasks that require logical thinking and multiple steps to solve, such as arithmetic or commonsense reasoning questions.

Other techniques​

  • Generated knowledge prompting
  • Least-to-most prompting
  • Self-consistency decoding
  • Complexity-based prompting
  • Self-refine
  • Tree-of-thought
  • Maieutic prompting
  • Directional-stimulus prompting

Prompt engineering - Wikipedia

Parameters​

Temperature​

Controls the randomness of the model's output. A higher temperature makes the output more random, while a lower temperature makes it more deterministic.

Understanding OpenAI's Temperature Parameter | Colt Steele

Other Topics​

  • Iterative
  • Summarizing
  • Inferring
  • Transforming
  • Expanding
  • Chatbot
  • Conclusion

ChatGPT Prompt Engineering for Developers - DeepLearning.AI

Assistant APIs​

The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling.

At a high level, a typical integration of the Assistants API has the following flow:

  1. Create an Assistant in the API by defining its custom instructions and picking a model. If helpful, enable tools like Code Interpreter, Retrieval, and Function calling.
  2. Create a Thread when a user starts a conversation.
  3. Add Messages to the Thread as the user ask questions.
  4. Run the Assistant on the Thread to trigger responses. This automatically calls the relevant tools.

Create AI Assistants with OpenAI's Assistants API

Knowledge based retrieval tool -

platform.openai.com/docs/assistants/overview

Learning​