Prompt Chaining
Week 2: Techniques in Prompt Engineering
Prompt Chaining: Breaking Down Complex Tasks
Learn how to link multiple prompts in sequence to handle complex tasks and multi-step reasoning.
Understanding Prompt Chaining
Prompt Chaining involves linking multiple prompts in a sequence to handle complex tasks. This approach breaks down the problem into smaller steps, each handled by the model before moving to the next. It's particularly effective in scenarios that require multi-step reasoning or complex problem solving.
Prompt Chaining enhances AI problem-solving capabilities by breaking complex tasks into manageable steps, allowing for more nuanced and accurate outputs.
Key Principles of Effective Prompt Chaining
- Simplicity: Ensure each step is clear and straightforward.
- Context Retention: Maintain and reference context across prompts.
- Iterative Refinement: Refine outputs at each step before moving forward.
- Error Handling: Include checks for consistency and correctness.
Prompt Chaining in Action
Here's how to apply Prompt Chaining effectively:
Example: Recipe Creation
We'll break down the task of creating a recipe into three steps: choosing ingredients, determining cooking method, and writing instructions.
from openai import OpenAI
client = OpenAI()
def get_ai_response(prompt):
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You are a professional chef."},
{"role": "user", "content": prompt}
],
temperature=0.7,
max_tokens=400
)
return response.choices[0].message.content.strip()
# Step 1: Choose ingredients
ingredients = get_ai_response("List 5 main ingredients for a healthy vegetarian dinner.")
# Step 2: Determine cooking method
cooking_method = get_ai_response(f"Suggest a cooking method for a dish using these ingredients: {ingredients}")
# Step 3: Write recipe
recipe = get_ai_response(f"Write a brief recipe using the ingredients {ingredients} and the cooking method {cooking_method}.")
print("\nIngredients:")
print(ingredients)
print("\nCooking Method:")
print(cooking_method)
print("\nRecipe:")
print(recipe)
Benefits of Prompt Chaining
- Improved Accuracy: Breaking down complex tasks leads to more accurate results.
- Better Control: Allows for fine-tuning at each step of the process.
- Transparency: Makes the problem-solving process more visible and understandable.
- Flexibility: Easily adaptable to various types of complex tasks.
Prompt Chaining enables tackling complex tasks that might be challenging for a single prompt, improving overall output quality and relevance.
Practice Exercises
Practice using Prompt Chaining to solve complex tasks by connecting multiple prompts in sequence.
Exercise 1: Story Creation
Use Prompt Chaining to create a short story by breaking it down into character creation, plot outline, and story writing.
from openai import OpenAI
client = OpenAI()
def get_ai_response(prompt):
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You are a creative writer."},
{"role": "user", "content": prompt}
],
temperature=0.7,
max_tokens=400
)
return response.choices[0].message.content.strip()
# Step 1: Character Creation
character = get_ai_response("Create a main character for a short story. Describe their name, age, and one unique trait.")
# Step 2: Plot Outline
plot = get_ai_response(f"Create a brief plot outline for a story featuring this character: {character}")
# Step 3: Story Writing
story = get_ai_response(f"Write a 150-word story using this character {character} and plot outline {plot}")
print("\nCharacter:")
print(character)
print("\nPlot Outline:")
print(plot)
print("\nStory:")
print(story)
Exercise 2: Problem-Solving Scenario
Use Prompt Chaining to solve a hypothetical business problem by breaking it down into problem analysis, solution brainstorming, and implementation planning.
from openai import OpenAI
client = OpenAI()
def get_ai_response(prompt):
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You are a business consultant."},
{"role": "user", "content": prompt}
],
temperature=0.7,
max_tokens=400
)
return response.choices[0].message.content.strip()
# Step 1: Problem Analysis
issues = get_ai_response("Analyze this problem: 'A small bookstore is struggling with declining sales due to online competition.' Identify three key issues.")
# Step 2: Solution Brainstorming
solutions = get_ai_response(f"Propose three potential solutions to address these issues: {issues}")
# Step 3: Implementation Planning
plan = get_ai_response(f"Create a brief implementation plan for this solution: {solutions.split('2.')[0]}")
print("\nKey Issues:")
print(issues)
print("\nPotential Solutions:")
print(solutions)
print("\nImplementation Plan:")
print(plan)
Summary
Prompt Chaining is a powerful technique for breaking down complex tasks into manageable steps, allowing for more nuanced and accurate outputs from AI models. By applying the principles of simplicity, context retention, iterative refinement, and error handling, you can tackle a wide range of complex problems effectively. Practice with different types of tasks to master this technique and enhance your AI interactions.