Generate Code & Literature with Google’s New AI Prompts

by kevin

Imagine you are tasked with understanding a large software project containing thousands of lines of code. How would you begin to comprehend it? Or consider the challenge of reviewing a complex pull request—how can you quickly grasp the core changes? Additionally, how would you explain your code to non-technical colleagues in a way that is easily understandable? Large software projects are inherently complex, and studies have shown that developers spend up to 70% of their time on program comprehension. These scenarios highlight a common challenge: how to quickly and accurately understand and convey the intent of code. Traditional methods, such as detailed comments or documentation, often prove to be either overly verbose or difficult to keep updated. Simply reading the code can also be time-consuming, especially when dealing with intricate logic.

Google’s research team has proposed an innovative solution to this challenge: utilizing AI to generate natural language outlines of code (NL Outlines). While this idea may seem straightforward, it possesses the potential to fundamentally change the way software development is conducted. By integrating natural language processing, machine learning, and software engineering, NL Outlines provide developers with an unprecedented means of understanding and interacting with code.

What are NL Outlines?

NL Outlines serve as high-level overviews of code functions, composed of concise natural language statements that effectively segment the code into logical parts and summarize the main ideas of each section. Unlike traditional code comments, NL Outlines are more structured and abstract, offering a “bird’s-eye view” of the code.

In the past, when faced with a complex function containing dozens of lines of code, the traditional approach was to read through it line by line to grasp every detail. With NL Outlines, developers can quickly skim through a few sentences of natural language descriptions to immediately understand the core logic and structure of the function. This not only accelerates code comprehension but also provides clear guidance for subsequent development and maintenance.

The above image illustrates an IDE interface integrated with NL Outlines functionality. Although the paper does not provide the specific implementation code for this IDE, it demonstrates how NL Outlines can be applied in a real development environment.

  1. The left panel displays the NL Outline of the function, providing a high-level overview of its functionality, including steps such as calculating the distance matrix, initializing paths, and iteratively adding nodes.
  2. The main editing area shows the specific code implementation of the nearest_neighbor_tour function.
  3. Comments corresponding to the left-side Outline are inserted into the code (marked in green), providing immediate contextual explanations.
  4. The top of the interface features a search bar, indicating the ability to perform code searches based on NL Outlines.

This interface demonstrates how NL Outlines can seamlessly integrate into the development environment, providing both a high-level overview of the code and maintaining close ties to the specific implementation. This design can help developers understand code structure faster, improve navigation efficiency, and provide contextual support during code writing and maintenance.

Unique Features of NL Outlines

One of the most exciting features of NL Outlines is their ability to maintain bidirectional synchronization with the code. When modifications are made to the code, the corresponding outline description updates automatically; conversely, adjusting the outline results in corresponding changes to the code. This dynamic association ensures consistency between code and documentation, addressing the long-standing issue of outdated documentation that developers often face.

NL Outlines can be presented in various forms, such as independent overviews or cleverly interspersed within the code, forming a modern “literary programming” style. This flexibility allows developers to quickly switch perspectives based on their needs, gaining both a high-level understanding and diving into code details.

With the assistance of advanced large language models (LLMs), NL Outlines can be generated automatically, eliminating the need for developers to write them manually. This not only saves time but also ensures the accuracy and consistency of the descriptions. More importantly, AI can capture code patterns and structures that humans might overlook, providing a more comprehensive and insightful overview.

How AI Understands and Summarizes Code

Google’s research team experimented with several top LLMs, including Gemini 1.0 Pro, Ultra, and Gemini 1.5. The results indicated that the Gemini 1.5 series performed best in generating NL Outlines, surpassing other models in both accuracy and quality of expression. This finding offers critical insights for prompt engineers: when selecting LLMs for code comprehension and generation tasks, the latest and largest models are not always the best choice. Specific capabilities of the model, the quality of training data, and the degree of task-specific fine-tuning are all essential factors.

To illustrate NL Outlines, consider the following example:

from openai import OpenAI
import json
import time

# Initialize DeepSeek AI client
client = OpenAI(api_key="sk-ee3", base_url="https://api.deepseek.com")

def gen(prompt):
    """Simulate the gen function of APPL, using DeepSeek AI to generate replies"""
    response = client.chat.completions.create(
        model="deepseek-chat",
        messages=[
            {"role": "system", "content": "You are a helpful assistant skilled in step-by-step reasoning."},
            {"role": "user", "content": prompt}
        ],
        max_tokens=1024,
        temperature=0.7,
        stream=False
    )
    return response.choices[0].message.content

def cot_sc(question: str, num_samples: int = 5):
    # Set system prompt and user question
    system_prompt = "You are a helpful assistant skilled in step-by-step reasoning."
    user_prompt = f"Question: {question}nLet's approach this step-by-step:"

    # Generate multiple CoT reasoning samples
    samples = [gen(user_prompt) for _ in range(num_samples)]

    # Extract final answers from each sample
    final_answers = []
    for sample in samples:
        # Parse the last line of each sample as the final answer
        final_answer = sample.split('n')[-1].strip()
        if final_answer.startswith("Therefore, "):
            final_answer = final_answer[len("Therefore, "):]
        final_answers.append(final_answer)

    # Consistency check
    consistency_prompt = "Now, let's analyze the consistency of our reasoning:n"
    for i, answer in enumerate(final_answers):
        consistency_prompt += f"Sample {i+1}: {answer}n"
    consistency_prompt += "Based on the above samples, the most consistent answer is:"

    # Generate final conclusion
    final_conclusion = gen(consistency_prompt)
    return final_conclusion

# Example usage
question = "If a train travels 120 km in 2 hours, what is its average speed in km/h?"
start_time = time.time()
result = cot_sc(question)
end_time = time.time()

print(f"Final conclusion: {result}")
print(f"Total time taken: {end_time - start_time:.2f} seconds")

The NL Outline for this code is as follows:

  1. Import necessary libraries and initialize the DeepSeek AI client.
  2. Define the gen function to simulate APPL’s generation functionality.
  3. Define the cot_sc function to implement the CoT-SC algorithm.
  4. Set the system prompt and user question.
  5. Generate multiple CoT reasoning samples.
  6. Extract the final answer from each sample.
  7. Perform a consistency check.
  8. Generate the final conclusion.
  9. Example usage and time measurement.

Main Explanations

  1. A gen function is defined to simulate APPL’s generation functionality, directly using the DeepSeek AI API.
  2. The cot_sc function implements the complete CoT-SC algorithm, including generating multiple samples, extracting answers, performing consistency checks, and generating the final conclusion.
  3. The standard Python list comprehension is used to generate multiple samples in parallel.
  4. Time measurement is added to understand the time required for the entire process.

This implementation retains the core logic and structure of the original code while avoiding dependency on the APPL library. It directly uses the DeepSeek AI API to generate responses, achieving similar functionality.

How AI Generates These Outlines

Generating high-quality NL Outlines is not a simple task. It requires AI to understand not only the syntax of the code but also its semantics and intent. Google’s research team utilized state-of-the-art large language models (like the Gemini series) to accomplish this task.

Generation Process

  1. Input Code: Input the code function that needs explanation into the AI model.
  2. Prompt Engineering: Use carefully designed prompts to guide the AI model in generating the desired outline.
  3. Outline Generation: The AI model analyzes the code and generates corresponding natural language descriptions.
  4. Post-Processing: Perform necessary cleaning and formatting of the generated outlines.

Two Main Generation Techniques

The research team explored two primary generation techniques:

  1. Interleaved Generation:
  • Method: The AI model is asked to insert outline comments directly into the original code.
  • Advantages: The generated results are immediately usable and tightly integrated with the code.
  • Disadvantages: There is a risk of accidentally modifying the original code, requiring additional validation steps.
  1. Line Number Infilling:
  • Method: The AI model generates outline statements along with their corresponding line numbers in the code.
  • Advantages: The generation process is more efficient and does not modify the original code.
  • Disadvantages: Additional processing steps are needed to integrate the outlines into the code.

Both methods have their pros and cons, and researchers found that they are suitable in different scenarios.

Analysis and Post-Processing

After generating NL Outlines, several parsing and post-processing steps are required to ensure quality:

  1. Error Detection: Check for common issues such as formatting errors, out-of-bounds line numbers, and duplicate line numbers.
  2. Content Filtering: Remove irrelevant or duplicate information to ensure the outline is concise and clear.
  3. Alignment Optimization: Adjust the position of outline statements to perfectly correspond with the code structure.
  4. Style Consistency: Standardize language expressions to ensure a consistent outline style across the entire project.

These steps not only enhance the quality of NL Outlines but also lay the groundwork for future applications and expansions.

The chart above illustrates the performance evaluation results of different large language models (LLMs) in generating NL Outlines, comparing the two main generation techniques: Interleaved Generation and Line Number Infilling. Evaluation dimensions include:

  1. Overall Quality
  2. Helpfulness
  3. Correctness
  4. Detail
  5. Style

The chart uses color coding to represent performance levels, from red (worst) to dark green (best).

Key Observations

  1. The Gemini 1.5 Pro and Flash models performed best across most metrics, particularly in overall quality, helpfulness, and correctness.
  2. The interleaved generation technique appeared to perform slightly better than line number infilling on certain models, especially in overall quality and helpfulness.
  3. All models performed relatively well in terms of correctness, indicating that the generated NL Outlines generally accurately reflect the code content.
  4. Scores for detail and style were relatively dispersed, possibly reflecting different evaluators’ subjective preferences in these areas.
  5. The DeepSeek-Coder-Instruct model performed relatively poorly on certain metrics, particularly with line number infilling.

These results suggest that the latest LLM models (like the Gemini 1.5 series) can generate high-quality, useful, and correct code outlines, providing developers with valuable tools for code comprehension. Additionally, different generation techniques may impact output quality, providing direction for further optimization of NL Outlines generation methods.

How NL Outlines Change the Development Process

Researchers have illustrated various applications of NL Outlines in software development, primarily divided into three areas: code understanding, code maintenance, and developer experience.

Key Considerations

  1. Code Understanding and Navigation: The most direct application of NL Outlines is to accelerate code comprehension. Imagine opening a new project or reviewing a colleague’s code without needing to read line by line; instead, you can quickly gain an understanding of the overall structure and key logic through concise natural language descriptions. This not only saves time but also helps developers get up to speed faster. In IDEs, NL Outlines can be integrated into symbol lists, providing an overview of functions. Clicking on outline statements can directly navigate to the corresponding code location, enabling precise navigation. Furthermore, outlines can be used for intelligent code folding, allowing developers to expand or hide code details as needed. We can expect plugins to emerge soon for integration into environments like VS Code.
  2. Code Maintenance and Refactoring: During code maintenance, NL Outlines serve as “living documentation.” When code changes, the outlines automatically update, ensuring that documentation remains in sync with the code. This significantly reduces the maintenance burden on developers, addressing the common issue of outdated comments. More interestingly, developers can guide code changes by editing the outline. For example, modifying an outline statement to express a high-level intent for a logical change can prompt AI to automatically complete the corresponding code modifications. This approach allows developers to think and operate at a more abstract level while maintaining control over the details.
  3. Code Generation and Prototyping: In terms of code generation, NL Outlines provide a new interactive model. Developers can first write or modify outlines and then let AI generate or adjust code based on those outlines. This method not only produces code that better meets expectations but also allows developers to exercise finer control and iteration during the generation process. This approach is particularly valuable for rapid prototyping, where developers can quickly generate workable code frameworks by writing high-level functional descriptions, then gradually refine and enhance them.
  4. Code Review: During code reviews, NL Outlines can significantly enhance efficiency. Reviewers can first examine the outline changes to quickly understand the main content and intent of code modifications before delving into specific code details. This not only speeds up the review process but also helps catch high-level design issues. For large changes or complex refactoring, NL Outlines can even automatically generate change summaries, aiding reviewers in better understanding and assessing the impact of modifications.
  5. Code Search and Reuse: NL Outlines open new possibilities for code searching. Developers can use natural language queries to search codebases, locating code snippets that implement specific functions or follow certain patterns. This semantic-based search is far more powerful and intuitive than traditional keyword searches. Additionally, NL Outlines can assist in code reuse. When developers need to implement similar functionalities, they can quickly find reference implementations by searching and comparing outlines, thereby improving development efficiency.

In conclusion, Google’s announcement of NL Outlines represents a significant leap forward in software development. By leveraging the power of large language models and AI, developers can now gain unprecedented insights into code structure, accelerate comprehension, and enhance collaboration. As this technology matures and becomes more widely adopted, we can expect to see a fundamental shift in how developers interact with and understand code. The future of software development is here, and it’s powered by natural language outlines.

Categories: AI Tools Guide
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