🧩 Breaking Big Problems into Tiny Pieces
The LEGO Analogy
Imagine you have a HUGE box of LEGO bricks. Someone asks you to build a castle. Scary, right? 😰
But wait! What if you:
- First build just one tower
- Then build another tower
- Then add a wall between them
- Then add a gate
Suddenly, building a castle is just building small pieces, one at a time!
That’s decomposition! Breaking a big, scary task into small, easy pieces.
🎯 What is Task Decomposition?
Task decomposition means splitting a big problem into smaller problems.
Real-Life Example
Big Task: “Clean your messy room”
Decomposed into:
- Pick up toys → Put in toy box
- Put dirty clothes → In hamper
- Make the bed
- Organize books on shelf
Each small task is easy. Together, they clean the whole room!
AI Example
Big prompt: “Write a business plan for a lemonade stand”
Decomposed:
Step 1: List what supplies you need
Step 2: Calculate how much they cost
Step 3: Decide your lemonade price
Step 4: Figure out how many cups to sell to make profit
The AI handles each small step perfectly!
📝 Subtask Prompts
Subtask prompts are the individual questions you ask for each small piece.
How It Works
Instead of ONE giant prompt, you send MANY small prompts:
graph TD A["Big Task"] --> B["Subtask 1"] A --> C["Subtask 2"] A --> D["Subtask 3"] B --> E["Answer 1"] C --> F["Answer 2"] D --> G["Answer 3"] E --> H["Final Result"] F --> H G --> H
Example: Writing a Story
Instead of: “Write me a story about a brave mouse”
Use subtask prompts:
| Subtask | Prompt |
|---|---|
| 1 | “Create a name and description for a brave mouse character” |
| 2 | “Describe a forest setting where the mouse lives” |
| 3 | “What problem does the mouse face?” |
| 4 | “How does the mouse solve the problem?” |
| 5 | “Write a happy ending” |
Each answer feeds into the next!
📋 Plan-and-Solve Prompting
Plan-and-Solve = First make a plan, THEN solve each step.
The Magic Words
Add this to your prompt:
“Let’s first understand the problem and make a plan. Then, let’s solve it step by step.”
Example: Math Word Problem
Problem: “Tom has 15 apples. He gives 3 to each of his 4 friends. How many does he have left?”
Plan-and-Solve Prompt:
Let's first understand the problem and
make a plan to solve it.
Problem: Tom has 15 apples, gives 3 to
each of 4 friends. How many left?
Make a plan, then solve step by step.
AI Response:
PLAN:
1. Find total apples given away
2. Subtract from original amount
SOLVE:
Step 1: 3 apples × 4 friends = 12 apples
Step 2: 15 - 12 = 3 apples
Answer: Tom has 3 apples left ✓
The plan prevents mistakes!
🪜 Least-to-Most Prompting
Start simple → Build complexity
Like climbing stairs: start at the bottom, go up one step at a time.
graph TD A["Simplest Question"] --> B["Slightly Harder"] B --> C["Even Harder"] C --> D["Full Question"] style A fill:#90EE90 style D fill:#FFD700
Example: Learning Fractions
Goal: What is 3/4 + 2/3?
Least-to-Most Approach:
| Level | Question |
|---|---|
| 🟢 Easy | “What is a fraction?” |
| 🟡 Medium | “How do you find a common denominator?” |
| 🟠 Harder | “Add 1/2 + 1/4” |
| 🔴 Full | “Now add 3/4 + 2/3” |
Each answer teaches something needed for the next question!
Why It Works
The AI “learns” from its own earlier answers. By the time you ask the hard question, the AI has all the knowledge ready!
🔗 Successive Prompting
One answer → Next question → Next answer → Next question…
It’s like a conversation that builds on itself!
The Chain
graph TD A["Prompt 1"] --> B["Answer 1"] B --> C["Prompt 2 uses Answer 1"] C --> D["Answer 2"] D --> E["Prompt 3 uses Answer 2"] E --> F["Final Answer"]
Example: Planning a Party
Prompt 1: “List 5 fun games for a birthday party”
Answer 1: Musical chairs, Pin the tail, Treasure hunt, Freeze dance, Balloon pop
Prompt 2: “For treasure hunt from your list, what supplies do I need?”
Answer 2: Clue cards, small prizes, box for treasure…
Prompt 3: “Write 5 clue cards for a backyard treasure hunt using those supplies”
Answer 3: Detailed clue cards!
Each step builds on the previous answer!
⏪ Backward Chaining
Start at the END and work BACKWARD!
The Idea
Instead of: “How do I start?” Ask: “What’s the last step before success?”
Then: “What step comes before THAT?”
Keep going until you reach the beginning!
graph RL D["Step 4: GOAL"] --> C["Step 3"] C --> B["Step 2"] B --> A["Step 1: START"] style D fill:#FFD700 style A fill:#90EE90
Example: Baking a Cake
Goal: Eat a delicious birthday cake
Backward Chain:
| Step | Question | Answer |
|---|---|---|
| 🎂 | “What’s the step before eating cake?” | Decorate it |
| 🎨 | “What’s before decorating?” | Let it cool |
| ❄️ | “What’s before cooling?” | Bake it in oven |
| 🔥 | “What’s before baking?” | Mix the batter |
| 🥣 | “What’s before mixing?” | Gather ingredients |
Now you have a complete plan, built backward!
When to Use Backward Chaining
- ✅ When you know the goal but not how to start
- ✅ When solving puzzles or mysteries
- ✅ When debugging code (start from error, trace back)
🎮 Summary: Pick Your Weapon!
| Method | Best For | Remember It |
|---|---|---|
| Task Decomposition | Big overwhelming tasks | “Break the LEGO castle into towers” |
| Subtask Prompts | Complex multi-part projects | “Many small questions > One big question” |
| Plan-and-Solve | Math and logic problems | “First plan, then solve” |
| Least-to-Most | Learning new concepts | “Climb the stairs of difficulty” |
| Successive Prompting | Building on previous answers | “Chain your questions together” |
| Backward Chaining | Goal is clear, path is not | “Start at the finish line” |
🌟 The Big Secret
All these methods share ONE magic trick:
Never try to eat the whole elephant. Take one bite at a time! 🐘
Whether you break tasks apart, climb from easy to hard, or work backward from your goal—you’re always making BIG problems SMALL.
And small problems? Those are EASY! 💪
🎯 Quick Practice
Try this prompt using Plan-and-Solve:
I want to learn guitar in 6 months.
Let's first understand this goal and
make a plan. Then break it into
monthly steps.
Watch how the AI creates a clear, step-by-step learning plan!
You’re now a Decomposition Master! 🏆