Prompt Chaining

Lesson Overview

In this video, you'll learn about prompt chaining, a technique that involves making a sequence of LLM calls to break down a larger problem into smaller, more manageable tasks. By focusing the model's attention on specific sub-problems, you can achieve higher-quality results and tackle more complex challenges.

• 00:00: Introduction to prompt chaining and its benefits
• 00:37: Example of a basic prompt chaining workflow
• 02:00: Using prompt chaining to improve output quality and write long-form content
• 03:58: Importance of carrying forward context in prompt chaining
• 05:06: Experimenting with different models for specific use cases

Key Concepts

Prompt Chaining

Prompt chaining is a technique where you make a sequence of LLM calls to achieve a specific output. By breaking down a larger problem into smaller, individual tasks, you give the model more opportunities to focus on specific aspects of the problem, leading to better overall results.

• Allows the model to break down complex problems into smaller, more manageable tasks
• Gives the model more "thinking time" to improve output quality
• Enables the creation of long-form content by progressively building on previous context

Context Preservation

When using prompt chaining, it's crucial to ensure that the context from previous steps is carried forward to subsequent LLM calls. This is achieved by inserting the output of previous steps into the user-assistant message pairs of the following steps.

• By default, new LLM prompts do not have context from previous steps
• Inserting the output variable (e.g., step2.output) into preceding LLM steps preserves context
• Context preservation creates the illusion of a continuous conversation, even though each request is a new call

Model Experimentation

Different models have varying strengths and weaknesses, and it's essential to experiment with various models to find the best fit for your specific use case. You can even mix and match models within a single prompt chain to achieve the most efficient and highest-quality results.

• Models can excel in different ways, regardless of benchmark performance
• Experimenting with different models for specific use cases is crucial
• Mixing models within a prompt chain can lead to more efficient and higher-quality results

Key Takeaways

1. Prompt chaining allows you to break down larger problems into smaller, more manageable tasks, enabling the model to focus on specific aspects and achieve better overall results.
2. By giving the model more "thinking time" through prompt chaining, you can improve the quality of the output and tackle more complex challenges, such as writing long-form content.
3. Carrying forward context from previous steps is crucial in prompt chaining. This is achieved by inserting the output variable of previous steps into the user-assistant message pairs of the following steps, creating the illusion of a continuous conversation.
4. Experimenting with different models for specific use cases is essential, as models can excel in various ways regardless of benchmark performance. Mixing models within a prompt chain can lead to more efficient and higher-quality results.
5. "Experimentation is so key. So models can beat benchmarks all the time in different ways and there's always progress, but so often a cheaper model or a model that maybe doesn't crush the benchmarks is actually way better for your specific use case."