Build Your Own AI Code Generator From Scratch — A PocketFlow Tutorial!

Ever wished you could just describe a coding problem and have an AI automatically generate comprehensive test cases, implement the solution, and iteratively improve it until everything works perfectly? This guide shows you how to build exactly that using the PocketFlow AI Code Generator Cookbook!

1. From Problem to Perfect Code: Why AI Code Generation Matters

Picture this: You're staring at a LeetCode-like coding problem, and instead of going through the usual grind of writing test cases, coding a solution, debugging failures, and repeating the cycle until everything works, you just paste the problem description and watch an AI system automatically:

1. Generate comprehensive test cases including all the edge cases you'd probably forget

2. Implement a clean solution based on the problem requirements

3. Test everything automatically with detailed pass/fail analysis

4. Intelligently debug by deciding whether the tests are wrong or the code needs fixing

5. Iterate until perfect - all tests passing with a working solution

This isn't science fiction - it's exactly what we're going to build together!

What you'll learn: By the end of this tutorial, you'll have built a complete AI development assistant that can take any coding problem and automatically work through the entire development cycle. We'll use PocketFlow to orchestrate the workflow, making the complex process surprisingly manageable and easy to understand.

2. How AI Code Generation Works: Step-by-Step with Two Sum

Wait, why not just ask ChatGPT? You might think: "Why build all this? Can't I just paste the problem into ChatGPT and get the answer?"

Sure, you could! But here's what typically happens:

• ChatGPT gives you one solution (no test cases!)

• If there's a bug, you have to spot it manually

• Edge cases? You better hope you thought of them

What we need instead is a smart workflow. An AI system that can write code, test it, catch its own mistakes, and fix them. Just like how human developers work: code → test → debug → improve → repeat.

In this tutorial, we'll learn hands-on how to build exactly this kind of intelligent workflow using the classic Two Sum problem as our example.

First, let's look at the problem we'll be solving:

Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.

Example: nums = [2,7,11,15], target = 9 → Output: [0,1]

Our Smart Workflow in Action

So how does our intelligent system actually work? Here's the high-level flow we're going to build:

Notice the key insight: when tests fail, we don't just give up. We analyze what went wrong and intelligently fix it, then test again. This creates a feedback loop that keeps improving until everything works.

Now let's see this workflow handle our Two Sum problem step by step!

Step 1: Generate Test Cases

The first thing our system does is create comprehensive test cases (way more than just the given examples):

=== Generated 7 Test Cases ===
1. Basic case: [2, 7, 11, 15], target=9 → [0, 1]
2. Duplicates: [3, 3], target=6 → [0, 1]  
3. Negatives: [-1, -2, -3, -4, -5], target=-8 → [2, 4]
4. Zero case: [0, 4, 3, 0], target=0 → [0, 3]
5. End solution: [1, 2, 3, 4, 5, 6], target=11 → [4, 5]
6. Larger array: [5, 75, 25, 45, 42, 2, 11, 9, 55, 12], target=14 → [2, 6]

Why this rocks: It automatically thinks of edge cases like duplicates, negatives, and larger arrays. No more forgetting to test the tricky scenarios!

Step 2: Implement Function

With test cases ready, our system writes clean, efficient code:

def run_code(nums, target):
    num_to_index = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in num_to_index:
            return [num_to_index[complement], i]
        num_to_index[num] = i
    return []

Cool detail: It chose the smart O(n) hash map approach instead of the obvious (but slow) nested loop. The AI actually thinks about efficiency!

Step 3: Run Tests

Time for the moment of truth - our system tests all cases in parallel:

=== Test Results: 6/7 Passed ===
Failed: Larger array case
Expected: [2, 6], Got: [0, 7]

Uh oh! One test failed. But here's where the magic happens...

Step 4: Intelligent Debugging

Instead of panicking, our system analyzes what went wrong:

• Array: [5, 75, 25, 45, 42, 2, 11, 9, 55, 12], target: 14

• Position 0: 5 + Position 7: 9 = 14 ✅ (what our code found)

• Position 2: 25 + Position 6: 11 = 36 ❌ (what the test expected)

Plot twist: The test case was wrong, not the code! Our system figures this out and fixes it:

=== Smart Fix ===
Test 6: Expected [2, 6] → [0, 7]

Step 5: Victory!

After the fix, our system runs the tests again:

=== Test Results: 7/7 Passed ===

The key insight: Instead of blindly "fixing" perfectly good code, our system correctly identified that the test case had a math error. This is what separates intelligent systems from basic automation.

This feedback loop is the secret sauce. The AI writes, tests, analyzes failures, and intelligently decides what to fix. Way better than crossing your fingers with a one-shot ChatGPT answer!

Ready to build this? Let's dive into the tools that make it possible.

3. Building Intelligent Workflows with PocketFlow: The Perfect Toolkit

Now that we understand the conceptual process, how do we actually build a system that can orchestrate these steps, handle the decision-making, and manage the iterative improvements? This is where PocketFlow becomes our secret weapon!

Think of PocketFlow as your development workshop manager. Just like a well-organized workshop has different workstations for different tasks, PocketFlow helps you create specialized "workers" for each job and coordinate them smoothly.

The Workshop Approach: Nodes as Specialist Workers

In PocketFlow, each major task becomes a Node - think of it as a specialist worker who's really good at one specific job:

class Node:
    def __init__(self): self.successors = {}
    def prep(self, shared): pass
    def exec(self, prep_res): pass  
    def post(self, shared, prep_res, exec_res): pass
    def run(self, shared): p=self.prep(shared); e=self.exec(p); return self.post(shared,p,e)

This is like a simple recipe: prep (gather ingredients), exec (cook), post (serve and decide what's next). Each Node knows its job and signals what should happen next.

The Shared Workspace: Information Hub

Think of this as a shared whiteboard where all workers can read and write:

shared = {
    "number": 10,
    "result": None
}

Each worker grabs what they need from this shared space and leaves their results for others.

Quick Example: Simple Math Pipeline

Let's build a tiny workflow that takes a number, adds 5, then multiplies by 2:

class AddFive(Node):
    def prep(self, shared):
        return shared["number"]

    def exec(self, number):
        return number + 5

    def post(self, shared, prep_res, result):
        shared["number"] = result
        print(f"Added 5: {result}")
        return "default"

class MultiplyByTwo(Node):
    def prep(self, shared):
        return shared["number"]

    def exec(self, number):
        return number * 2

    def post(self, shared, prep_res, result):
        shared["result"] = result
        print(f"Final result: {result}")
        return "default"

Each worker does one simple job and passes the result along.

Connecting Workers: The Assembly Line

# Connect workers in sequence (the >> means "then go to")
add_worker = AddFive()
multiply_worker = MultiplyByTwo()

add_worker >> multiply_worker

# Create the manager and run it
math_flow = Flow(start=add_worker)
shared = {"number": 10}
math_flow.run(shared)

# Output:
# Added 5: 15
# Final result: 3

It's like saying: "First add 5, then multiply by 2."

The Beauty: Each worker is simple and focused. Need to change the math? Just swap out a worker. Want to add more steps? Connect more workers. The smart behavior comes from how they work together, like musicians in an orchestra!

For our AI code generator, we'll have workers like:

• TestCaseGenerator: Creates comprehensive test suites

• CodeImplementer: Writes clean implementations

• TestRunner: Executes tests efficiently

• SmartReviewer: Analyzes failures and decides fixes

Same simple pattern, just with more sophisticated jobs.

4. Building the Code Generator with PocketFlow Nodes

Now let's build the actual workers! Each one handles a specific job in our development workflow.

The Shared Memory Structure

Our workers communicate through a simple dictionary that holds everything needed for the code generation workflow:

shared = {
    "problem": "Problem description...",
    "test_cases": [{"name": "Basic case", "input": {...}, "expected": "result"}],
    "function_code": "def run_code(...): ...",
    "test_results": [{"passed": True, "error": None}],
    "iteration_count": 0
}

Think of this as a desk where each worker leaves notes for the next person. We need the original problem description, test cases to validate our solution, the actual code implementation, test results to know what passed or failed, and an iteration counter to prevent infinite loops.

Worker 1: The Test Case Generator

class GenerateTestCases(Node):
    def prep(self, shared):
        return shared["problem"]

    def exec(self, problem):
        prompt = f"Generate test cases for: {problem}\nOutput as YAML..."
        response = call_llm(prompt)
        return yaml.safe_load(response)

    def post(self, shared, prep_res, result):
        shared["test_cases"] = result["test_cases"]
        print(f"Generated {len(result['test_cases'])} test cases")

This worker reads the problem, asks an AI to create test cases in YAML format, parses the response, and stores the test cases. Simple as that!

Worker 2: The Code Implementer

class ImplementFunction(Node):
    def prep(self, shared):
        return shared["problem"], shared["test_cases"]

    def exec(self, inputs):
        problem, test_cases = inputs
        prompt = f"Implement: {problem}\nTests: {test_cases}\nFunction must be named 'run_code'"
        response = call_llm(prompt)
        return yaml.safe_load(response)

    def post(self, shared, prep_res, code):
        shared["function_code"] = code
        print("Function implemented")

This worker takes the problem and test cases, asks the AI to write code, and stores the result. The AI sees both the problem AND the tests, so it writes better code.

Worker 3: The Test Runner (Batch Processing)

class RunTests(BatchNode):
    def prep(self, shared):
        code = shared["function_code"]
        tests = shared["test_cases"]
        return [(code, test) for test in tests]

    def exec(self, test_data):
        code, test_case = test_data
        output, error = execute_python(code, test_case["input"])
        return {
            "test_case": test_case,
            "passed": output == test_case["expected"] and not error,
            "actual": output,
            "error": error
        }

This uses BatchNode to test all cases one-by-one. Each test gets the code and one test case, runs it safely, and returns the result.

The post method handles the results:

    def post(self, shared, prep_res, results):
        shared["test_results"] = results
        passed = sum(1 for r in results if r["passed"])
        print(f"Tests: {passed}/{len(results)} passed")

        if all(r["passed"] for r in results):
            return "success"
        elif shared.get("iteration_count", 0) >= 5:
            return "max_iterations"
        else:
            return "failure"

This counts passes/fails, prints results, and signals what to do next: celebrate success, give up after too many tries, or fix the problems.

Worker 4: The Smart Reviewer

class Revise(Node):
    def prep(self, shared):
        failures = [r for r in shared["test_results"] if not r["passed"]]
        return {
            "problem": shared["problem"],
            "code": shared["function_code"], 
            "failures": failures
        }

    def exec(self, data):
        prompt = f"Problem: {data['problem']}\nCode: {data['code']}\nFailures: {data['failures']}\nWhat should I fix?"
        response = call_llm(prompt)
        return yaml.safe_load(response)

This worker gathers all the context about what failed, asks the AI to analyze and decide what to fix, then returns structured fixes.

    def post(self, shared, prep_res, fixes):
        if "test_cases" in fixes:
            # Update specific test cases
            for index, new_test in fixes["test_cases"].items():
                shared["test_cases"][int(index)-1] = new_test

        if "function_code" in fixes:
            shared["function_code"] = fixes["function_code"]

        shared["iteration_count"] = shared.get("iteration_count", 0) + 1
        print("Applied fixes")

This applies the AI's suggested fixes to either test cases, code, or both. The AI might say "test case 3 has wrong expected output" or "the algorithm needs a different approach."

Connecting Everything Together

def create_code_generator_flow():
    # Create workers
    generate_tests = GenerateTestCases()
    implement = ImplementFunction()
    run_tests = RunTests()
    revise = Revise()

    # Connect the assembly line
    generate_tests >> implement >> run_tests
    run_tests - "failure" >> revise >> run_tests

    return Flow(start=generate_tests)

The magic happens in the connections: normal flow goes generate → implement → test. But if tests fail, we go to the reviewer, who fixes things and sends us back to testing.

Why This Works: Each worker is laser-focused on one job. The intelligence emerges from their coordination - like a jazz band where everyone knows their part but can improvise together!

5. Conclusion: Code Smarter, Not Harder!

And there you have it! You've journeyed from staring at a coding problem to building an AI that generates tests, writes code, and even debugs its own mistakes. We peeked under the hood to see how problems get understood (test generation), solved (smart implementation), tested (batch execution), and improved (intelligent revision), all in a smooth, iterative loop.

Hopefully, this tutorial has shown you that building intelligent coding assistants doesn't have to feel like rocket science. With a friendly toolkit like PocketFlow, complex AI workflows become way more manageable. PocketFlow is like your helpful conductor, letting you focus on the fun part – what each piece of your AI system should do – instead of getting tangled in the tricky coordination. It's all about breaking big ideas into small, focused Nodes and letting the Flow make sure they all work together intelligently.

Now, it's your turn to grab the keyboard and get creative! We really encourage you to explore the PocketFlow AI Code Generator Cookbook we've built together. Play around with it, maybe try "Reverse Linked List" or "Valid Parentheses" and watch how it tackles different problems. What if you made the test generator more creative, or added performance benchmarking? What other coding challenges can you automate?

The world of AI-assisted development is evolving rapidly, and with tools like PocketFlow, you're all set to jump in and be a part of it. Go on, give your next coding project an AI brain – we can't wait to see what intelligent tools you build!

Ready to explore more or need some help? You can dive deeper by checking out the main PocketFlow repository on GitHub for the core framework, other neat examples, and all the latest updates. The complete code for the AI code generator we discussed is right in the pocketflow-code-generator directory within the PocketFlow cookbook.