DeepSeek’s Chain of Thought: Debugging a Python Prime Number Function
DeepSeek’s Chain of Thought: Debugging a Python Prime Number Function
Introduction: Why Problem-Solving Approaches Matter
One of the most fascinating things about AI models isn’t just that they can answer questions—it’s how they arrive at their solutions. While many AI models, including ChatGPT, tend to provide polished answers, DeepSeek’s Chain of Thought (CoT) approach allows us to peek into its reasoning process. This makes it feel more like a real-time problem solver rather than a magic black box.
Today, we’ll examine a Python debugging challenge where DeepSeek walks through its problem-solving process step by step, correcting a broken function that’s supposed to return prime numbers.
The Problem: Debugging a Prime Number Function
A developer wrote the following Python function, intending for it to return the first n prime numbers. However, when they call get_primes(5), expecting [2, 3, 5, 7, 11], something goes wrong.
def get_primes(n):
primes = []
num = 2
while len(primes) < n:
for i in range(2, num):
if num % i == 0:
break
primes.append(num) # This is likely where the bug is!
num += 1
return primes
print(get_primes(5)) # Expected output: [2, 3, 5, 7, 11]
The output, however, includes non-prime numbers. What’s wrong, and how do we fix it?
DeepSeek’s Chain of Thought (Full Debugging Session)
Below is the AI’s full thought process:
Okay, let's take a look at this Python function. It's supposed to return
the first n prime numbers, but it's not working correctly. The user says
that when they print get_primes(5), they expect [2, 3, 5, 7, 11], but
maybe the current code isn't doing that. Let me try to figure out what's
wrong.
First, let me read through the code step by step. The function get_primes
starts with an empty list called primes. The variable num is initialized
to 2. Then there's a while loop that runs as long as the length of primes
is less than n. Inside that loop, there's a for loop that goes from 2 to
num (but not including num). For each i in that range, it checks if num
is divisible by i. If it is, it breaks out of the loop. Then, regardless
of whether it broke or not, it appends num to primes and increments num by 1.
Wait a minute, that's the problem! The code appends num to primes even
if it found a divisor. Because after checking all possible divisors
(from 2 to num-1), if any of them divides num, it breaks. But even if
it breaks (meaning num is not a prime), the code still appends it to
primes. That's definitely wrong. The append should only happen if the
for loop completes without finding any divisors.
For example, take num=4. The for loop runs i=2. 4%2 ==0, so it breaks.
Then primes.append(4), which is incorrect. So primes would collect
non-primes. Hence, the output would include composite numbers like
4, 6, etc., which is not right.
So the main issue is that the code appends num to primes in every iteration
of the while loop, without checking whether it actually passed the
prime test. The fix here would be to only append num if no divisors
were found.
How to do that? Well, in Python, a for loop can have an else clause
that executes when the loop completes normally (i.e., not broken by
a break statement). So, in the current code, the for loop checks
if num is divisible by any i. If it finds a divisor, breaks. If it
goes through all i's and doesn't break, then the else block is executed.
So, the code should append num to primes only in the else case.
Looking at the current code, after the for loop, primes.append is called
unconditionally. That's the mistake. Instead, primes.append should be
inside the else clause of the for loop.
So the corrected code would move the append into the else block.
Let me adjust the code.
DeepSeek’s Corrected Code
def get_primes(n):
primes = []
num = 2
while len(primes) < n:
for i in range(2, num):
if num % i == 0:
break
else: # This else executes only if the loop was *not* broken
primes.append(num)
num += 1
return primes
print(get_primes(5)) # Output: [2, 3, 5, 7, 11]
Analyzing DeepSeek’s Thought Process
DeepSeek’s approach is remarkably structured and human-like in a way that makes it easy to follow. Here’s what stands out:
1. It reads the code like a human would.- It first walks through each line, describing what it does.
- It narrates its understanding rather than jumping to conclusions.
- It picks num=4 as an example and explains why it incorrectly gets added.
- It generalizes this to show how non-prime numbers are mistakenly appended.
- Many developers (especially beginners) don’t know about for-else.
- DeepSeek uses it naturally, explaining why it works in this case.
- It brings up the idea that checking up to sqrt(n) would be more efficient.
- But it wisely decides to focus on correctness first, since efficiency isn't the main issue.
- After making the correction, it mentally steps through the execution.
- This kind of validation process is crucial in real-world debugging.
Comparing DeepSeek and ChatGPT CoT
DeepSeek’s reasoning process is far more transparent than ChatGPT’s typical CoT approach. Here’s why:
| Feature | DeepSeek’s CoT | ChatGPT’s CoT |
|---|---|---|
| Thinking Out Loud | Yes—fully narrates thoughts | Limited—tends to summarize |
| Exploration of Alternatives | Yes—considers and rejects ideas | No—usually presents a final answer |
| Step-by-Step Debugging | Yes—tests its own fix | Rare—fix is often assumed correct |
| Teaching Approach | High—explains logic deeply | Medium—gives direct solutions |
| Self-Correction | Yes—second-guesses and refines | No—typically presents only final thoughts |
DeepSeek’s approach feels like watching an experienced developer debug rather than just seeing a final answer. This makes it incredibly valuable for learning.
Final Thoughts: Why This Matters for Developers
This level of transparent reasoning makes DeepSeek an amazing tool for:
- Learning debugging strategies instead of just getting answers.
- Seeing alternative solutions and understanding why they work.
- Practicing structured thinking, which is key for coding interviews and real-world problem-solving.
Need ChatGPT or DeepSeek Expertise?
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