03 - Advanced Prompting Techniques

Sophisticated methods like chain-of-thought and few-shot learning

In this chapter, we'll delve into sophisticated prompting methods that can significantly enhance the performance and versatility of LLMs for complex tasks.

3.1 Chain-of-thought prompting

Chain-of-thought prompting is a technique that encourages the LLM to break down complex problems into step-by-step reasoning processes.

Example:

Solve the following word problem, showing your step-by-step reasoning:

Problem: If a train travels 120 miles in 2 hours, how far will it travel in 5 hours assuming it maintains the same speed?

Step 1: [Your reasoning here]
Step 2: [Your reasoning here]
...
Final Answer: [Your answer here]

This technique is particularly useful for mathematical problems, logical reasoning, and complex decision-making tasks.

3.2 Few-shot learning

Few-shot learning involves providing the LLM with a few examples of the desired input-output pattern before asking it to perform a similar task.

Example:

Convert the following dates to DD/MM/YYYY format:

Input: March 15, 2023
Output: 15/03/2023

Input: July 4, 1776
Output: 04/07/1776

Now, convert this date:
Input: December 31, 1999
Output:

This technique helps the LLM understand the specific pattern or format you're looking for in the output.

3.3 Zero-shot learning

Zero-shot learning is the ability of LLMs to perform tasks they weren't explicitly trained on, based solely on the task description in the prompt.

Example:

Without using any external knowledge, classify the following sentence into one of these categories: Sports, Technology, or Politics.

Sentence: "The new quantum computer can solve complex algorithms in seconds."
Classification:

This technique leverages the LLM's general knowledge to perform tasks without specific examples.

3.4 In-context learning

In-context learning combines elements of few-shot learning with more extensive context to guide the LLM's behavior and output.

Example:

You are a helpful assistant named Claude. You always respond in a polite and professional manner, and you never use explicit language. You're knowledgeable but admit when you're not sure about something. Please respond to the following user query in character:

User: Hey Claude, what's the deal with quantum entanglement?

Claude:

This technique is powerful for creating consistent persona-based interactions or specialized domain expertise.

3.5 Hands-on exercise: Implementing advanced techniques

Now, let's practice using these advanced techniques:

1. Use chain-of-thought prompting to solve a multi-step math problem.
2. Create a few-shot learning prompt to teach the LLM a new text transformation task.
3. Develop a zero-shot classification prompt for categorizing movie genres.
4. Design an in-context learning prompt that makes the LLM act as a specific type of expert.

Example solution for #2:

Transform the following sentences by replacing all nouns with their plurals:

Input: The cat sat on the mat.
Output: The cats sat on the mats.

Input: A child played with the toy in the park.
Output: Children played with the toys in the parks.

Now transform this sentence:
Input: The scientist conducted an experiment in the laboratory.
Output:

These advanced techniques allow you to tackle more complex tasks and achieve more nuanced and accurate outputs from LLMs. As you practice, you'll develop a sense for which techniques work best for different types of tasks and how to combine them effectively.