# Extracting Insights from a Problem Statement

How to extract insights from a problem statement to solve a coding problem?

Here are some tips on extracting insights from a problem statement to help solve a coding problem:

Identify the core problem being solved - boil it down to the simplest form. This helps frame potential solutions.

Make connections to similar patterns or problems you’ve seen before. Related solutions often translate.

Note explicit constraints or requirements called out. These will guide assumptions and design decisions.

Look for ambiguous or vague areas that need clarification before coding. Get clear on edge cases.

Determine how you will validate a solution meets the requirements. This influences testing.

Break the problem into modular pieces that can be solved independently before integrating.

Categorize parts of the problem by paradigm like search, simulation, data transformation etc. Different paradigms lead to different algorithms.

Diagram, visualize or abstract parts of the problem to get alternative perspectives.

Consider examples that fit the problem definition vs edge cases that stress test boundaries.

Relate concepts from computer science fundamentals that may provide algorithmic approaches.

The goal is to rigorously break down, analyze and categorize the problem from multiple angles to extract salient insights that guide coding in a meaningful direction.

**Identify Keywords**: Read the problem statement carefully and highlight action words like “sort,” “count,” “find,” or “determine,” as well as constraint words like “minimum,” “maximum,” “distinct,” or “unique.”**Understand Inputs/Outputs**: Clearly note what the input parameters and expected output are. Recognize their types (integer, string, array, etc.) and any limitations or constraints.**Gauge Scale and Complexity**: Look for clues about the expected scale of the problem. Large inputs often signal the need for a more efficient algorithm.**Determine Objectives**: Understand what needs to be achieved by breaking the problem down into its primary goals. For example, if you need to “find the shortest path,” your primary objective is minimizing some sort of cost.**Spot Relationships**: See if the problem statement mentions relations between elements, like “each element in array A corresponds to an element in array B.”**Pinpoint Special Conditions**: Make note of any special conditions that can affect the solution, such as “array is sorted” or “no two elements are the same.”**Recognize Sub-Problems**: Many problems can be broken down into smaller, more manageable parts. Identify these to make problem-solving easier.**Operational Requirements**: Sometimes, the problem specifies how the output should be presented. For example, “return the result modulo 10^9 + 7” or “output should be sorted.”**Identify Edge Cases**: Problem statements often hide edge cases in the example section or hint at them through specific phrasing, such as “for any integer n.”**Time/Space Complexity**: Although usually not directly stated, try to infer acceptable time and space complexity from the given problem constraints.

By extracting these insights, you’ll have a much clearer understanding of the problem and a solid foundation for crafting your solution.

Create prompts that I can use to extract insights from problem statement when talking to GPT AI.

Here are some prompts that can help extract insights from a problem statement when discussing with an AI assistant:

Clarifying ambiguity:

- Could you identify any ambiguous or vague parts of the problem statement that need clarification?

Isolating core problem:

- How would you summarize the core problem being solved in simple terms?

Determining validation approach:

- What tests or validation criteria could we use to ensure the code meets the requirements?

Categorizing paradigms:

- Does this problem relate most closely to search, graph algorithms, data transformation, simulation, or some other paradigm?

Relating core concepts:

- What computer science or programming concepts like data structures, algorithms, or design patterns might be useful for solving this problem?

Visualizing components:

- Could you diagram or visually represent aspects of this problem to provide a different perspective?

Generating examples:

- What are some examples that fit the problem definition well? What are some edge cases that stress test boundaries?

Breaking into sub-problems:

- How could we break down this problem into modular sub-problems or components that can be solved independently?

These prompts aim to elicit insights from different angles - constraints, validation, categorization, connections, visualization, examples, decomposition. The goal is rigorously analyzing the problem statement.

- What are the key action words in the problem statement?
- Can you identify the input parameters and their types?
- What is the expected output and its type?
- Are there any constraints mentioned on the input parameters?
- What is the expected scale of the problem in terms of data size or computational complexity?
- What are the primary objectives I need to achieve to solve this problem?
- Are there any relationships between elements that are explicitly mentioned or implied?
- What special conditions or scenarios should I be aware of?
- Can this problem be broken down into smaller sub-problems? If yes, what are they?
- Are there operational requirements for presenting the output?
- Can you spot any edge cases or examples that hint at edge cases?
- What is the acceptable time and space complexity for solving this problem, based on the insights from the problem statement?