ChatGPT and Bard

ChatGPT Vs Bard: ChatGPT vs. Bard: A Comparative Analysis for Coding

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Exploring Generative AI for Programming: ChatGPT vs. Bard

Generative AI has become an invaluable asset for programmers, offering assistance in writing and debugging code, ultimately streamlining our workflows. However, with the emergence of competing tools like ChatGPT and Bard , determining the ideal choice can be challenging. In this article, we embark on an in-depth comparison between these two platforms, aiming to identify the most feature-rich solution for programming tasks. By analyzing factors such as functionality, usability, and performance, we aim to provide developers with valuable insights to aid in their decision-making process. Join us as we unravel the nuances of ChatGPT and Bard, unveiling which tool reigns supreme in the realm of Generative AI for programming.

ChatGPT vs. Bard

ChatGPT and Bard: What’s the difference?

Distinguishing ChatGPT from Bard lies primarily in the Large Language Models (LLMs) powering them. ChatGPT harnesses the might of Generative Pre-trained Transformer 4 (GPT-4), whereas Bard relies on the Language Model for Dialogue Applications (LaMBDA). Furthermore, while OpenAI develops ChatGPT, Bard is a brainchild of Google.

Regarding functionality, both exhibit strikingly similar capabilities. Programmers can leverage ChatGPT for:

  1. Suggestions: Both models excel in suggesting correct syntax and parameters for various code constructs.
  2. Completion: It adeptly finishes code that you’ve initiated.
  3. Debugging: It assists in pinpointing errors and issues in your code.
  4. Explanation: It proficiently elucidates the code it generates or the code you input.

Both models underwent rigorous training on extensive datasets, comprising Common Crawl, Wikipedia, books, articles, documents, and scraped internet content. Nevertheless, Bard diverges slightly in its training approach, focusing on web-based conversations and dialogues, unlike ChatGPT, which predominantly learned from general content scraped from the web.

Both products remain in active development, with Bard slightly more so than ChatGPT. To illustrate the practical implications of these variances, let’s examine their performance when pitted against each other.

Testing ChatGPT and Bard against each other

We opted for seven categories to pit the chatbots against each other, chosen based on what we deemed most pertinent: code generation, problem-solving, code refactoring, debugging aid, third-party plugins/UI extension, user-friendliness, and cost. It’s important to note that this isn’t an exhaustive scientific evaluation but rather a blend of select tests coupled with our firsthand experiences thus far.

  1. Code Generation

    The cornerstone of our requirements, isn’t it? To evaluate this aspect, we provided the following prompt: “Create a Python function that generates a list of 100 prime numbers.”

  2. Bard’s coding endeavor


    ChatGPT vs. Bard

Seems promising, but is it effective? From our observations, chatbots occasionally produce non-functional code, sometimes even conjuring up non-existent functions! This underscores the peril of deploying AI-generated code into production without human supervision. Let’s put it to the test and find out.ChatGPT vs. Bard

3 . ChatGPT’s coding attempt


ChatGPT vs. Bard

When prompted with the same query, ChatGPT generates code that defines a function generate_primes(n), which accepts an integer n as input and returns a list of prime numbers up to n. It employs the Sieve of Eratosthenes algorithm to compute the primes. Both outputs yield functional results. ChatGPT’s output stands out in this instance for its cleanliness and brevity. Moreover, it accurately interprets our ambiguous prompt. However, it’s imperative to note that this is just one scenario. Chatbots’ responses can vary significantly based on factors such as the programming language employed (less common or documented languages may result in more errors) or the complexity of the query. Let’s delve into how Bard and ChatGPT handle diverse use cases.

Winner: ChatGPT 🏆

2. Problem Solving

Let’s examine how these Large Language Models (LLMs) tackle a problem. Instead of a generic function, we’ll present them with a programming challenge from LeetCode:

Compose JavaScript code to augment all arrays, enabling the invocation of the array.last() method on any array, returning its last element. If the array is empty, the method should return -1.

ChatGPT vs. Bard

Not only does ChatGPT solve the problem, but it explains the problem. Neat! Let’s run the code through leetcode and see if it actually passes the tests, though.
ChatGPT vs. Bard

It looks like ChatGPT solved the problem on the first try! Now it’s time for Bard to step up to the plate.

Bard’s problem solving

ChatGPT vs. Bard

Alright, Bard didn’t delve deep into explaining the code’s workings. Yet, intriguingly, there’s a striking resemblance between what ChatGPT and Bard churned out. Were they peeking at each other’s assignments?

ChatGPT vs. Bard
Naturally, we can’t take Bard’s word at it, so we run the test through Leetcode, and…

ChatGPT vs. Bard

Voila! As anticipated, the outcome aligns closely with ChatGPT’s response. When it comes to tackling challenges, the decision between the two becomes quite tricky. Despite yielding nearly identical outcomes, ChatGPT excels in elucidating the code, facilitating a deeper understanding—an invaluable asset for acquiring problem-solving skills rather than mere reliance. This mirrors our consistent encounter with ChatGPT: not only does it propose plausible solutions, but it also guides you through the process more comprehensively.

Champion: ChatGPT 🏆

Third-party plugins & UI extensions

By integrating a third-party plugin into an LLM, we unlock a realm of possibilities, enabling functionalities such as executing code within chat conversations or seamlessly integrating with applications like Zapier.

As a beta feature, ChatGPT currently offers over 80 plugins to its premium subscribers. For a glimpse into some of these plugins, explore our article: “The top ChatGPT plugins for developers.” Here’s a snapshot of ChatGPT’s plugin store at present:

ChatGPT vs. Bard

And now, behold Bard’s plugin store:

…Unfortunately, there’s nothing to showcase at the moment! While there are whispers about its potential existence in the future, there’s no concrete timeline as of now.

If you prefer not to navigate through the web interface, both ChatGPT and Bard offer APIs. However, Bard’s API remains restricted to invitation-only access, preventing us from conducting a thorough examination. On the contrary, ChatGPT boasts a comprehensive and accessible API. Additionally, ChatGPT surprises users with its remarkably functional official mobile application, which proves to be quite handy during brainstorming sessions.

In this comparison, ChatGPT earns the point, as Bard either lacks these features presently or has yet to unveil them outside an invite-only circle.

Champion: ChatGPT 🏆

Conclusion: ChatGPT wins out, but both tools are useful!

In the ongoing battle with a score of four to three, ChatGPT emerges as the overall champion (👑). However, in practical terms, both these tools are indispensable in your arsenal. Let’s delve into some essential considerations for developers harnessing these tools:

When it comes to the base version, ChatGPT operates solely on LLM, which might render its information outdated. On the other hand, Bard utilizes both LLM and search data. Interestingly, ChatGPT is gearing up to integrate “Search with Bing” into its free offering soon.

In the realm of content generation, ChatGPT usually outshines with its knack for producing documentation.

Bard, on the contrary, often crafts more elaborate explanations of code.

It’s worth noting that Bard imposes a conversation length constraint, whereas ChatGPT restricts requests over time (GPT-4).

While these tools are incredibly handy, it’s imperative to maintain a solid understanding of the code you’re working on. Avoid excessive reliance, as the accuracy of results isn’t guaranteed at all times. Until we meet again, happy coding!t


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