Skip to main content

Prompt Engineering Techniques

Techniques for Prompt Engineering

The following techniques help improve the precision and relevance of responses generated by language models.

Zero-shot

  • Definition: The model responds without related examples.

  • Use: Useful for broad, general tasks.

  • Example:

    • Prompt: "Explain what a relational database is."
    • Expected Response: The model responds based on general knowledge without providing examples.

Few-shot

  • Definition: Includes brief examples in the prompt to guide the response.

  • Use: Improves accuracy by providing specific context.

  • Example:

    • Prompt: "Write a Python function that calculates the average of a list of numbers. Input example: [10, 20, 30]. Expected output: 20."
    • Expected Response: The model follows the provided pattern to generate the response.

Chain-of-Thoughts (CoT)

  • Definition: The model is guided to think step-by-step, explaining its reasoning.

  • Use: It is useful for tasks that require detailed explanation and logic.

  • Example:

    • Prompt: "Explain step-by-step how to implement JWT authentication in a Flask application."
    • Expected Response: The model details each process step, such as library installation, route configuration, token generation, etc.

Your technique here

  • Definition: A custom technique you can create to meet a specific need.

  • Example: Role-based Prompting (Creating prompts based on roles or perspectives).

    • Prompt: "You are a virtual tour guide. Describe the Eiffel Tower to a group of tourists, including its history and fun facts."
    • Expected Response: The model responds as a tour guide, providing a more engaging and relevant description.

Use Cases for Prompt Engineering

Guiding Virtual Assistants

  • Developing prompts to help virtual assistants deliver useful and contextually relevant responses.

Content Creation

  • Utilizing prompts to produce articles, summaries, or product descriptions.

Automating Tasks

  • Designing prompts to automate repetitive tasks, such as generating reports or conducting data analysis.

Other techniques for Prompting

1. Chat History

It's essential to remember that your chat history can impact the answers you receive during a chat session. For example, if you're asking questions about a Java application and then suddenly switch to asking about Python applications, the answers you receive may need to be more helpful and accurate.

  • If you want to change the subject of the conversation, it's best to start a new chat rather than simply clearing the chat history. Clearing the chat history removes the messages from the page, but creating a new chat resets the conversation history and can result in better answers.

  • If you want to change the subject, click on a new chat to start fresh.

2. Chat way

Chat in phases. Ask questions, wait for the answer, and depending on that, ask for more details. See below:

  • How do I implement this?
  • Bring more information on this.
  • Change this to that.
  • Get this class and do this.

Enhancing Prompts with StackSpot AI

Knowledge Source

Creating a Knowledge Source is crucial as it helps provide context to StackSpot AI. If you have already created one and are asking questions that don't yield the desired results, check if your Knowledge Source has enough information and context.

See below some bits of advice:

1. When creating a knowledge source, it's best only to include the information that you need and separate it by subject. StackSpot AI selects and ranks documents based on their relevance to your topic, so if you have a lot of unrelated information in one source, you may not get accurate results.

2. Instead of grouping different code snippets, frameworks, and programming languages in one Knowledge Source, it's better to create separate ones for each context. It allows StackSpot AI to find more relevant and in-depth documents for each subject, resulting in a wider variety of high-quality documents.

3. See examples:

  • Knowledge Source with Kafka Integration snippets.
  • Knowledge Source with best practices for functional programming.

4. Consider organizing Knowledge Sources to improve search results.

5. You can add specific Knowledge Sources to your Workspace's context.

Learn More

Was this page helpful?