Advanced Techniques: Chaining and Memory

For complex tasks, you can chain prompts or simulate memory to get better results. Advanced techniques help you tackle multi-step or context-heavy problems, and are especially useful for building sophisticated applications or workflows.

Chaining Prompts

Break a big task into smaller steps and use the output of one prompt as the input for the next. This approach allows you to build up complex results in a controlled, modular way.

Example:

1. Prompt 1: "Summarize the following article."
2. Prompt 2: "Based on the summary, list three key challenges discussed."
3. Prompt 3: "For each challenge, suggest a possible solution."

Simulating Memory

Remind the AI of previous context within your prompt. Since most models don't have persistent memory, you need to include relevant information from earlier steps in your input.

Example:

Earlier, you recommended three books: 'Dune,' 'The Hound of the Baskervilles,' and 'The Martian.' For each book, explain why it matches the user's interests in science fiction and mystery.

Best Practices for Advanced Techniques

• Plan your workflow: Outline the steps before writing prompts.
• Pass relevant context between steps to maintain continuity.
• Test each step individually before combining them.
• Use advanced techniques for tasks like multi-turn conversations, data extraction, or decision-making processes.