Control Flow: Conditionals & Loops

Control flow determines what code runs and how many times. Every program you write will use if/else and loops.

Learning Objectives

Write if / elif / else conditionals
Use for loops with range(), lists, and enumerate()
Use while loops and understand when to pick for vs while
Use break and continue to control loop execution
Use the match/case statement (Python 3.10+)
Build logic for real AI engineering decisions

Conditionals: if / elif / else

Conditionals let your code make decisions. Python uses indentation (4 spaces) instead of braces.

python conditionals.py

# Basic if/else
status_code = 200

if status_code == 200:
    print("Success!")
elif status_code == 404:
    print("Not found")
elif status_code == 429:
    print("Rate limited")
else:
    print(f"Unhandled status: {status_code}")

python conditions_ai.py

# Real AI engineering example: model routing
tokens_needed = 800
budget_limit = 0.05

if tokens_needed < 500 and budget_limit > 0.01:
    model = "gpt-4o-mini"        # cheap for simple tasks
elif tokens_needed < 4000 and budget_limit > 0.03:
    model = "gpt-4o"             # mid-range
elif tokens_needed >= 4000:
    model = "claude-sonnet-4.6-sonnet"   # long context
else:
    model = "gpt-4o-mini"        # fallback

print(f"Selected model: {model}")

💡

Python's truthiness saves lines

Instead of if len(items) > 0:, write if items:. Instead of if name != "":, write if name:. Python checks truthiness automatically.

For Loops

For loops iterate over sequences — lists, strings, ranges, dictionaries. You'll use them constantly.

python for_loops.py

# Loop over a list
models = ["gpt-4o", "claude-sonnet-4.6", "gemini-2.5-pro"]
for model in models:
    print(f"Testing {model}...")

# Loop with range(start, stop, step)
for i in range(5):
    print(i)            # 0, 1, 2, 3, 4

for i in range(2, 10, 3):
    print(i)            # 2, 5, 8

# Loop with index using enumerate()
for idx, model in enumerate(models):
    print(f"{idx + 1}. {model}")
# 1. gpt-4o
# 2. claude-sonnet-4.6
# 3. gemini-2.5-pro

# Loop over a dictionary
pricing = {"gpt-4o": 0.005, "gpt-4o-mini": 0.00015}
for model, price in pricing.items():
    print(f"{model}: ${price}/1K tokens")

# Loop over a string
for char in "Hello":
    print(char)          # H, e, l, l, o

While Loops

While loops repeat as long as a condition is true. Use them when you don't know how many iterations you need.

python while_loops.py

# Retry pattern — very common with LLM APIs
import time

max_retries = 3
attempt = 0
success = False

while not success and attempt < max_retries:
    attempt += 1
    print(f"Attempt {attempt}...")
    # Simulate API call
    response = call_api()
    if response.get("ok"):
        success = True
    else:
        time.sleep(2 ** attempt)  # exponential backoff

if not success:
    print("All retries failed")

⚠️

Infinite loops

If the while condition never becomes False, the loop runs forever. Always ensure the condition will eventually change, or include a break. If your program hangs, press Ctrl+C to stop it.

break and continue

python break_continue.py

# break — exit the loop entirely
for model in available_models:
    if is_healthy(model):
        selected = model
        break        # stop searching, we found one

# continue — skip this iteration, move to the next
documents = ["doc1.pdf", "corrupt.pdf", "doc3.pdf"]
for doc in documents:
    if doc == "corrupt.pdf":
        continue    # skip this one
    process(doc)

# Pattern: find first valid item
for attempt in range(5):
    result = try_action()
    if result:
        break

Pattern Matching (Python 3.10+)

The match/case statement is Python's version of switch/case. Great for handling different API response types.

python match_case.py

# Pattern matching for API status handling
def handle_response(status_code):
    match status_code:
        case 200:
            return "OK"
        case 201:
            return "Created"
        case 400:
            return "Bad Request"
        case 401:
            return "Unauthorized"
        case 429:
            return "Rate Limited"
        case 500 | 502 | 503:
            return "Server Error"
        case _:
            return "Unknown"     # default case

ℹ️

for vs while — which to use?

Use for when you know what you're iterating over (a list, range, dict keys).
Use while when you don't know how many iterations (retry until success, process until queue empty).
When in doubt, use for — it's safer (can't accidentally infinite loop).

🧪 Exercises

📝

Exercise 1 — FizzBuzz

Write a loop from 1 to 30. For each number: if divisible by 3, print "Fizz"; if by 5, print "Buzz"; if by both, print "FizzBuzz"; otherwise print the number.

📝

Exercise 2 — API Retry with Backoff

Write a while loop that simulates retrying an API call up to 5 times. Use import random; success = random.random() > 0.7 to simulate a 30% success rate. Print each attempt and wait 1 second between retries. Print "Success!" or "Failed after 5 attempts."

📝

Exercise 3 — Model Cost Comparison

Given a dict pricing = {"gpt-4o": 0.005, "gpt-4o-mini": 0.00015, "claude-sonnet-4.6": 0.003} and a prompt_tokens = 2000, loop through all models and print: "Model: gpt-4o — Cost: $0.0100". Identify the cheapest.

⚠️ Common Mistakes

✗

Using = in conditions

if x = 5: is a SyntaxError. You mean if x == 5:. This mistake is so common that Python deliberately makes = in conditions illegal.

✗

Off-by-one with range()

range(5) gives 0–4, NOT 0–5. The stop value is exclusive. range(1, 5) gives 1–4. Think of it as "up to but not including."

✗

Modifying a list while iterating over it

Never remove or append to a list you're currently looping through. Create a new list or use a list comprehension instead.

✅ You've completed this step when you can confirm:

You can write if/elif/else chains with multiple conditions You can iterate over lists, dicts, and ranges with for loops You use enumerate() when you need the index You can write a while loop for a retry pattern You understand when to use break vs continue