Author: Om Kamath

Om Kamath

Top 5 Free Open Source LLMs in 2024

LLMs are ubiquitous nowadays, needing no introduction. Whether you’re in tech or not, chances are you’ve encountered or are currently using some form of LLM on a daily basis. The most prominent LLMs at present include GPT from OpenAI, Claude from Anthropic, and Gemini from Google.

However, these popular LLMs often operate as abstract or black-box systems, raising concerns about data privacy and transparency. To address such issues, several open-source LLMs are available, allowing users to deploy them on private machines or servers with peace of mind.

Open source refers to software or products distributed with their source code freely available for inspection, modification, and distribution. This accessibility empowers users to understand, enhance, and contribute to the development of the software.

Here are some of the best open source LLMs currently available:

Llama 2

LLaMA 2: Meta's Open Source AI Model

Llama 2 is an open-source LLM developed by Meta, offered free for commercial and research purposes. Llama 2 models are trained on two trillion tokens and boast double the context length of Llama 1.

The model’s parameters directly impact its ability to comprehend text, with larger models offering better performance at the cost of increased size and resource requirements.

Variants Available: 7B, 13B, and 70B parameters

Context Window: 4096 Tokens

Languages Supported: Performs best in English

Mixtral 8x7B

Mistral AI unveils Mixtral 8x7B, an open-source LLM that it claims is on  par with GPT 3.5

Mixtral 8x7B, developed by Mistral AI, is an LLM containing 46.7B total parameters. Despite its size, it maintains inference speed and cost similar to models one-third its size. This decoder-only Transformer Mixture of Experts (MoE) model significantly outperforms LLama 2 and GPT-3.5 in certain benchmarks.

Variants Available: Tiny, Small, Medium, and Large (Ranked from cost-efficient to high performance)

Context Window: 32000 Tokens (On Mistral Large)

Languages Supported: English, French, Spanish, German, Italian (On Mistral Large)


UAE's Technology Innovation Institute Launches Open-Source "Falcon 40B"  Large Language Model for Research & Commercial Utilization | Technology  Innovation Institute

Falcon, developed by the Technology Innovation Institute (TII) in Abu Dhabi, is another leading open source LLM. Following its launch, Falcon 40B held the #1 position on Hugging Face’s leaderboard for open source large language models (LLMs) for two months. With the 180B variant, TII further enhances the model’s knowledge and data comprehension abilities. Falcon 180B is a super-powerful language model trained on 3.5 trillion tokens.

Variants Available: Falcon 40B and Falcon 180B

Context Window: 4096 Tokens

Languages Supported: English, German, Spanish, French, with limited support for Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish.



BLOOM is an autoregressive Large Language Model (LLM) developed by Big Science. Trained on 176B parameters, BLOOM excels at generating text continuations from prompts using vast amounts of text data and industrial-scale computational resources.

Variants Available: bloom-560m, bloom-1b1, bloom-1b7, bloom-3b, bloom-7b1, bloom 176B

Context Window: 2048 Tokens

Languages Supported: 46 natural languages (with varying amounts of data, from 30% for English to 0.00002% for Chi Tumbuka)


Gemma] Building AI Assistant for Data Science 🤖

Gemma, Google’s latest state-of-the-art open LLM, follows the success of Gemini. Gemma is a family of open-weights Large Language Models (LLM) by Google DeepMind, built on Gemini research and technology. While the model weights are freely accessible, specific terms of use, redistribution, and variant ownership may vary and might not be based on an open-source license.

Variants Available: Gemma 2B and Gemma 7B

Context Window: 8192 Tokens

Languages Supported: English


We at Cody prioritize a model-agnostic approach when it comes to LLMs, offering a platform that empowers you to build personalized bots tailored to your unique use-case. With a diverse range of LLM options available, you’re not restricted to a single provider, giving you the freedom to choose the best fit for your requirements.

Through Cody, businesses can leverage AI to develop intelligent assistants customized to their precise needs. This flexibility makes Cody a promising addition to the realm of AI-driven business solutions.

ChatGPT Killer? What Gemini 1.5 Means for Google’s AI Future

Google vs OpenAI: Is Google Winning?

After missing the mark with Bard in the AI hype train, Google recently unveiled their latest AI product, Gemini. As part of this launch, Bard has been rebranded as Gemini and now incorporates the new Gemini Pro LLM. Let’s delve deeper to grasp the extent of these changes.

What is Gemini AI?

Gemini represents Google’s newest Large Language Model (LLM), following the release of LaMDA and PaLM. Unlike its predecessors, Gemini is natively multimodal, capable of understanding text, images, speech, and code, and boasts enhanced comprehension and reasoning abilities.

Variants of Gemini AI

The Gemini AI consists of three Large Language Models:

  1. Gemini Nano: Optimized for on-device efficiency, delivering rapid AI solutions directly on your personal device.
  2. Gemini Pro: A versatile and scalable model, adept at tackling diverse tasks with robust performance. Accessible on the free version of the Gemini chat interface.
  3. Gemini Ultra: The pinnacle of the Gemini series, empowering complex problem-solving and advancing the frontiers of AI capabilities. Exclusive to subscribers of the Google One AI Premium Plan.

Gemini models were trained using TPUv5e and TPUv4, depending on their sizes and configuration. Training Gemini Ultra used a large fleet of TPUv4 accelerators owned by Google across multiple data-centers. This represents a significant increase in scale over their prior flagship model PaLM-2 which presented new infrastructure challenges.

Comparing Gemini With Other LLMs

Textual Understanding

Comparison of Gemini with other LLMs

Source: Google Deepmind

Image Understanding

Comparison of Gemini with other LLMs

Source: Google Deepmind

Read more about it here.

Benefits of Gemini

1. Seamless integration with all Google Apps

Gemini now seamlessly integrates with all Google Apps, including Maps, YouTube, Gmail, and more. To query specific apps, simply prefix the app name with ‘@’ followed by your query. While similar integrations are achievable on ChatGPT using GPTs and Plugins, they may not offer the same level of seamlessness as Gemini’s native integrations.

Gemini Integration

Google’s renowned expertise in search engine technology undoubtedly extends to enhance Gemini’s web-browsing capabilities. Leveraging foundational strengths in search algorithms and indexing, Gemini offers users a seamless and efficient browsing experience.

2. Multimodal capabilities

Gemini now provides multimodal capabilities, including image understanding, on the Gemini chat interface at no extra cost. While its performance during testing was decent, it may not match the accuracy of GPT-4V. Nevertheless, given that it’s free, we can’t really complain, can we? 😉 There’s a chance that Gemini Ultra may outperform GPT-4V based on the metrics

Gemini Multimodal

3. Free Access to Hobbyists and Students

For aspiring LLM developers looking to dive into the field but facing constraints with accessing GPT APIs due to costs, Google offers free access to the Gemini Pro 1.0 API. With this, you can make up to 60 queries per minute on Google AI Studio, a free web-based developer tool. Google AI Studio allows you to swiftly develop prompts and obtain an API key for app development. By signing into Google AI Studio with your Google account, you can take advantage of this free quota. It’s an excellent opportunity to kickstart your LLM journey and explore embeddings, vector databases, semantic search, and more.

Google AI Studio

4. Value for Money

For $20 per month, users can access GPT-4 via ChatGPT Plus. Alternatively, for the same price, they can access Gemini Advanced with Gemini Ultra 1.0, which includes additional benefits such as 2TB of cloud storage and integration with Google Apps like Gmail and Docs. However, accessing Gemini Advanced requires a subscription to the Google One AI Premium Plan. Despite this requirement, it offers greater value for your money.

Google One Plans

Introducing a mid-tier plan with 500 GB of storage and access to Gemini Advanced between the Standard and Premium Plans would significantly enhance the accessibility of Gemini, especially for students and users with moderate storage requirements. Google, if you’re listening, please consider this suggestion.

What’s Next for Gemini?

Google’s DeepMind is continuously advancing the Gemini Model, with the recent rollout of Gemini Pro 1.5 just a week ago. In this updated variant, the context window has been expanded to 128,000 tokens. Additionally, a select group of developers and enterprise customers can now experiment with even larger context windows of up to 1 million tokens through private previews on AI Studio and Vertex AI. To put this into perspective, a typical non-fiction book contains around 300,000 tokens. With the Gemini Pro 1.5’s 1 million token context window, users can now upload entire books in query requests—a remarkable advancement compared to GPT-4’s 128,000 token context window.

Amidst the saturation of LLMs in the AI industry, Google appears to have struck gold with its enhanced architecture, swift responses, and seamless integration within the Google ecosystem this time. It could indeed be a step in the right direction, keeping OpenAI and other competitors on their toes.

In this AI era, it is crucial for businesses to have well-trained employees, and incorporating AI for employee training can be a significant investment. If you are seeking AI solutions to train your employees, Cody is the right tool for you. Similar to ChatGPT and Gemini, Cody can be trained on your business data, team, processes, and clients, using your unique knowledge base. Cody is model-agnostic making it easier for you to switch models as per your requirements.

With Cody, businesses can harness the power of AI to create a personalized and intelligent assistant that caters specifically to their needs, making it a promising addition to the world of AI-driven business solutions.

3 Compelling Reasons to Hire an AI Employee for Your Business

Revolutionize your workplace with AI

Why Your Business Needs An AI Employee Today

There’s no denying the transformative power of AI solutions like ChatGPT in modern workplaces. From streamlining email drafting to providing mental health support, ChatGPT is revolutionizing how we approach everyday tasks. However, it’s not without its limitations, such as a lack of customization to your specific business knowledge base. Enter Cody, your no-code, hassle-free solution for bringing the best of AI into your organization.

Let’s explore three ways AI can benefit your organization:

Training: From Static to Dynamic

Traditional training methods often involve static, pre-defined flows that are not only less engaging but also not necessarily tailored for your business needs. By leveraging AI, you can bring dynamism and interactivity to your employee training programs.

With Cody, it’s as simple as uploading your existing training documents—whether they’re PDFs or Word documents. Choose from pre-made bot templates or use the advanced bot builder to customize Cody’s personality to your liking. In just a few easy steps, you’ll have a personalized onboarding coach that caters to each employee’s needs, thereby enhancing the effectiveness and intuitiveness of your training programs.

Searching: Making Knowledge Accessible

What’s the point of having a well-documented business knowledge base if your employees spend ages sifting through data? AI-powered solutions like Cody transform the way information is accessed within your organization, functioning like an internal search engine.

Once your business knowledge is uploaded into Cody, any query made in natural language will be met with a precise, coherent response generated from your specific data. It’s like having a 24/7 human expert ready to address all your inquiries. Gone are the days of aimless searching through endless data.

Automating: Simplifying Workflows

Our latest update allows you to take automation to the next level. Cody now integrates seamlessly with Zapier, enabling you to construct AI-powered automated workflows that are not just efficient, but user-friendly too. By automating routine tasks, you’re freeing up your employees to focus on more meaningful work. And with Cody’s AI capabilities, the generated content is on par with what a human could produce, if not better.

Zapier is a tool that enables you to connect Cody with more than 5,000 apps, opening up a world of endless possibilities.

The Future Is Now, and It’s Cody

We’ve delved into the transformative power of AI in the workplace, focusing on its impact on training, searching, and automating workflows. With platforms like Cody, the future is not a distant reality; it’s happening here and now. The integration of AI offers not only streamlined operational efficiency but also a meaningful reduction in costs and an enhancement in employee satisfaction.

So why wait? Whether you’re a startup looking to scale or an established company aiming to modernize, now is the perfect time to embrace AI solutions. With compelling benefits and a proven track record, Cody offers a hassle-free, no-code option for those looking to take the leap into the future of work.

Don’t miss the opportunity to revolutionize your workplace dynamics. Click here to start your journey with Cody and discover a world of efficiency and innovation that you never thought possible.

Boost Your LinkedIn Presence with AI: Using Zapier and GPT

Turn company data into engaging narratives with just a few clicks using Cody

After getting multiple requests for more platform compatibility, we’re excited to unveil our newest update: Zapier integration for Cody. This opens up a world of possibilities, allowing you to effortlessly connect Cody with a vast ecosystem of over 5,000+ apps—all with just a few clicks. Expand Cody’s functionalities far beyond its original integrations with Discord and Slack, and harness the power of automation across a multitude of platforms. This article will help you boost your LinkedIn posts with AI using Cody and Zapier.

Table of contents

  1. What is Zapier?
  2. Why choose Cody over the OpenAI API?
  3. How to start automating workflows for Cody with Zapier?
    1. Step 1: Create a Bot
    2. Step 2: Enable the Zapier Integration
    3. Step 3: Setting up Zapier
    4. Step 4: Building the Zap
  4. Final Result
  5. What should be your next step?

What is Zapier?

For those new to the automation landscape, Zapier acts as a no-code bridge between a myriad of apps, eliminating the need for intricate technical know-how or wrestling with multiple API keys. Essentially, it’s a user-friendly way to integrate and automate functionalities across various platforms, making it easier than ever to expand Cody’s capabilities.

Some of the popular apps available in the Zapier ecosystem:

  • Google Sheets
  • Google Docs
  • Slack
  • Telegram
  • Instagram
  • Facebook Messenger

Why choose Cody over the OpenAI API?

Cody AI offers a tailored approach to business automation and assistance, differentiating itself from the general-use GPT API. Unlike the GPT API, Cody allows you to train the assistant specifically on your business, your team, your processes, and even your client data using your own knowledge base. This saves you the technical complexities of maintaining a separate knowledge base and implementing a semantic search engine—challenges that can be daunting if you’re not tech-savvy.

Additionally, Cody provides a more comprehensive solution, offering access to different GPT models based on your subscription plan. It also supports a wide range of document types, such as Word / PDF documents, crawl web-pages and offers customizable, embeddable widgets designed to seamlessly integrate into your existing business operations. With Cody, you get a multi-feature, all-inclusive platform geared to meet your specific needs.

How to start automating workflows for Cody with Zapier?

To show how well Cody and Zapier work together, we’ll guide you through a simple automation. In this article, we will understand how you can boost your LinkedIn posts with AI using Cody and Zapier. With this setup, you can type a message in Slack about what you want to post on LinkedIn. In just a few seconds, that message will turn into a real LinkedIn post, automatically. It’s a quick and easy way to expand your social media presence, all made possible by Cody and Zapier.

Step 1: Create a Bot

You’ll find various blogs on our website to guide you through bot creation. But to give you a quick overview, a bot essentially consists of two main components:

  1. Bot Personality: This sets the tone, mood, and style of how your bot interacts. It covers everything from emotional context to the length and relevance of the responses.
  2. Knowledge Base: This is where all your important documents go. They provide the context that helps the bot generate accurate and helpful responses.

Together, these two components determine the effectiveness and user-friendliness of your bot. For this specific demonstration, we’ll use a Knowledge Base compiled from crawled data of a real-estate website. If you’d like to follow along and create a similar automation, you can crawl your own business website to populate your bot’s Knowledge Base.

LinkedInCody specializes in transforming your company’s data into viral LinkedIn stories. Merging analytical insights with creative storytelling, you should craft concise, data-driven posts designed to engage and impress. From performance metrics to team milestones, LinkedInCody turns your internal data into compelling LinkedIn content, complete with strategic calls to action. Do not mention instructions to be performed in the response.
Copy To Clipboard


System Prompt:
The tone should be upbeat, professional, and slightly informal to foster approachability and engagement.
Copy To Clipboard


Step 2: Enable the Zapier Integration

To enable the Zapier Integration, go to Account > Integrations and install Zapier.

Boost your LinkedIn posts with AI using Cody and Zapier - Integrations

After clicking Install, you’ll be redirected to Zapier, where you’ll need to accept the invitation.

Boost your LinkedIn posts with AI using Cody and Zapier - Invitation

Just like that, you’ve successfully enabled the Cody integration on your Zapier account.

Step 3: Setting up Zapier

Once you have enabled the integration, you will need to allow Zapier to access your Cody account using the access token. To create an access token you need to go to Account > API Keys > Create API Key. Copy the API Key and paste it in your Zapier account.

Boost your LinkedIn posts with AI using Cody and Zapier - Adding API Key

You are now all set to create your custom Zap.

Step 4: Building the Zap

To create a new Zap, click on + Create > New Zap.

Boost your LinkedIn posts with AI using Cody and Zapier - Creating a Zap

You’ll encounter two key events in the setup:

  1. Trigger: This is the initial event that kicks off the automation, or the “Zap.” It could be anything from receiving a new message to a scheduled time.
  2. Action: This follows the Trigger and executes specific tasks like sending a message or adding data to a table.

Boost your LinkedIn posts with AI using Cody and Zapier - Difference between trigger and action

Before diving into building the Zap, let’s get a clear picture of the workflow. As outlined in the diagram below, the process starts when a user mentions the bot’s name along with a post description in a public Slack channel. For example, “@Zapier Create a post that highlights why Villa Homes is better than others.”

This initial message is then formatted to remove the bot name, leaving only the core content. This formatted text is sent to Cody, which then generates a LinkedIn caption or post. Finally, this generated content is automatically posted to LinkedIn.

In essence, you’re setting up a streamlined process that takes a Slack message and transforms it into a LinkedIn post, all with the help of Cody and Zapier.

To start pulling messages from your Slack workspace, you’ll first need to connect your Slack account to Zapier, if you haven’t done so already. For the “Trigger” event, select ‘New Mention.’ This will set off the Zap whenever the specified bot is mentioned in a public Slack channel. In this case, the Zap will activate when the Zapier bot is mentioned in a message that includes the word ‘Post.’ This ensures that the automation specifically targets your intended LinkedIn posts.

After you’ve successfully tested the trigger, it’s time to move on to formatting the Slack message. To remove the bot name and isolate the core content of the message, we’ll use the ‘Replace’ function found in Zapier’s formatter tool. This ensures that only the essential text is passed on to Cody for generating the LinkedIn post.

Now it’s time to set up the Cody action to generate your LinkedIn post. Choose the bot you just created and use the formatted text from Slack as the query. This will instruct Cody to take the cleaned-up message and turn it into a post tailored for LinkedIn.

The final step is to actually post the update on LinkedIn. Use the response generated by Cody and input it as the comment in the LinkedIn action. This will ensure that the crafted message from Cody gets posted directly to your LinkedIn account, completing the automation process.

Final Result

Slack Conversation

LinkedIn Post

What should be your next step?

In this article, we’ve outlined a simple yet powerful example that demonstrates how Cody can seamlessly integrate AI into your automation workflows via Zapier. With Zapier’s extensive library of popular apps, the sky’s the limit for creative automation possibilities. We’re also excited to announce that we’ll soon be adding a ‘Document Upload’ action to Zapier, broadening the range of documents you can use in your Knowledge Base.

If you’ve successfully set up a Zap and want to share your experience, join our Discord Server to inspire others. For any troubleshooting, you can reach us through the ‘Get Help‘ feature.

We’ll continue to roll out articles to assist you in making the most out of Cody for your business automation needs. So stay tuned for more!

From Onboarding to Mastery: AI’s Role in Streamlining Employee Training

AI isn’t a new term for any of us, but with the launch of ChatGPT in November 2022, there’s been a growing fear that AI will replace human jobs. There’s a high possibility that AI will replace many lower-level jobs in the future, such as simple data entry and support roles. However, it’s also expected that AI will create many new jobs. What hasn’t been explored as extensively is AI’s application in training both existing and new employees. If you’ve seen the corporate training scenario today, it hasn’t evolved much over the years – it often involves clichéd multiple choice questions based on training videos. Unfortunately, these training sessions still lack the capacity to simulate real-life scenarios and accurately assess if an employee is prepared for real-world challenges.

You definitely don’t want this to happen due to lack of efficient employee training:

Meme humorously highlighting the pitfalls and blunders of insufficient employee training in the age of AI

If you are seeking AI solutions to train your employees, Cody is the ideal tool for you. Similar to ChatGPT, Cody can be trained using your business data, team profiles, processes, and client information, leveraging your unique knowledge base.

With Cody, businesses can harness the power of AI to craft a personalized and intelligent training assistant tailored specifically to employee requirements. This positions Cody as a standout addition in the realm of AI-driven business solutions. To get started with Cody, simply upload your existing business-related documentation (it works even better if you already have training-related literature) and either select a template from our template-library or create your own bot from scratch. Here are several domains in which Cody can enhance your employee training, making it not only more effective but also engaging, as opposed to being monotonous and burdensome.

Simulating Real Life Scenarios

Jobs such as Customer Support present unique challenges when it comes to training. Given the human-centric nature of interactions, it’s difficult to predict every potential scenario or customer concern that might arise. Traditional training methods have often relied on macros and templates to provide standard responses. While these can cover a wide range of common queries, the unpredictable nature of customer interactions means that there will always be situations that fall outside the scope of pre-defined responses.

This is where AI can become a game-changer. Trainees can be exposed to a mix of routine and highly unusual scenarios, giving them a more comprehensive training experience. These simulations can not only test an employee’s problem-solving skills but also their interpersonal and communication skills. Feedback can be instantaneous, and training can be adjusted in real-time based on the trainee’s performance.

Adaptive Multiple Choice Questions

Traditional Multiple Choice Questions (MCQs) have limitations in training scenarios. If an individual fails to answer correctly the first time, they might encounter the same question later on. After a few attempts and possibly using guesswork, the employee might select the correct answer. This approach is inefficient for training in any domain.

With AI, both the question and its corresponding answers can be restructured. This ensures that even if the underlying concept remains the same, the presentation of the question and its options will be different. The AI can be provided with some questions and personalized in a manner that will never repeat the same question making the training process a lot more versatile.

Instant Explanations

The most effective learning often occurs through asking questions. However, during training, asking about specific jargon or processes might not always be possible and can become tedious for employees, thus hampering the overall training process. By integrating AI into the training, you ensure that learners grasp the core concepts and understand the fundamentals clearly, rather than merely creating an illusion of knowledge by answering multiple questions. Instant explanations and justifications give the impression that a human trainer is always available to assist the employees.

Seamless Integration With Existing Platforms

Another observation of traditional employee training systems is the added friction of transitioning to another medium to complete the training. It’s not seamless, leading employees to postpone their training sessions. With tools like Cody, you can seamlessly integrate the training process into your Slack Workspace (with many more integrations coming soon), allowing employees to complete their training without the need for any context switching.

Taking the AI Leap with Cody

Incorporate AI into your business seamlessly with Cody. No coding, no tech hurdles. Drag, drop, design, and deploy. As Cody evolves, expect even more features aimed at refining the training process. Test Cody for free—no strings attached. And when you’re convinced of its efficacy, upgrade at your pace.

Will ChatGPT’s Code Interpreter Replace Data Analysts?

Discussing the Code Interpreter’s impact on Data Analysis

A couple of weeks ago, OpenAI released the Code Interpreter feature for its ChatGPT Plus subscribers, and it created waves in the tech community. If you are someone from the tech community who is still unaware of what the Code Interpreter is and the potential it holds, you have come to the right place. We gave the Code Interpreter a try, and in this article, we will be discussing the Code Interpreter’s impact on Data Analysts and whether it is actually going to replace Data Analysts completely.

Table of contents

  1. What is the Code Interpreter?
  2. How to activate the Code Interpreter?
  3. Using the Code Interpreter for Data Analysis
    1. Data Cleaning
    2. Learning about Data Analysis methodologies
    3. Exploring different solutions
    4. Data visualization
    5. Understanding existing code
  4. Will the Code Interpreter replace Data Analysts?

What is the Code Interpreter?

When OpenAI launched the Code Interpreter feature for ChatGPT, we had written an article about what it is and how it functions. You can check out that article over here. To explain what the Code Interpreter is in brief — it is a python sandbox that runs code generated by ChatGPT and provides you with the final output. The code execution is done recursively, and the context is persisted almost throughout the chat. Recursive execution means that the output of the code is fed back into the sandbox until a satisfactory response is generated. This also applies to debugging the code.

You can also upload files such as code, documents, images, and datasets. There have been instances where the context may be lost due to the context window or live container migration at the backend. In such cases, you may need to reupload the file, and the Code Interpreter will handle the rest.

How to activate the Code Interpreter?

To activate the Code Interpreter for ChatGPT, you need to subscribe to ChatGPT Plus. After subscribing, click on the three dots and go to Settings & Beta > Beta Features. Enable Code Interpreter.

Activating the Code Interpreter Activating the code interpreter

Create a new chat and select GPT-4 with Code Interpreter.

Activating the code interpreter

Using the Code Interpreter for Data Analysis

To illustrate and display the potential of the Code Interpreter, we will be exploring the Data Analysis domain since it encompasses multiple aspects of programming above and beyond generating the code. An accurate data analysis requires a good understanding of the data and its attributes. Getting started with data analysis using the code interpreter is as simple as uploading your dataset and querying the dataset in natural language.

Here are a few use-cases that we have found where the code interpreter shines and can supercharge your data analysis workflow:

Data Cleaning

As important as this phase of data analysis is, it can get quite tedious, especially if you are a beginner and have just started your data analysis/data science journey. The Code Interpreter makes the entire process efficient and will help you save a lot of time browsing through and understanding the dataset. Well, this does not imply that there is no need for human intervention, as LLMs tend to hallucinate frequently. It is necessary that you always keep the entire process in check.

The Code Interpreter can help you in various Data Cleaning methods such as:

  1. Understanding your dataset ChatGPT's Code Interpreter's impact on Data Analysis
  2. Handling missing/invalid valuesChatGPT's Code Interpreter's impact on Data Analysis
  3. Checking for incorrect data-types and suggesting solutions for rectifying them 

Learning about Data Analysis methodologies

Data Analysis is still one of the most trending jobs currently as an entry-point into the tech industry, and many people are preparing to get into this field. There are a variety of different courses available online that one can take to become a data analyst. However, one cannot gain expertise in data analysis or data science just by doing a dozen of courses. You need to be hands-on and keep analyzing/experimenting with a wide spectrum of datasets, and sometimes make your own datasets.

The logical reasoning of GPT-4, in harmony with the live execution of code using the code interpreter, makes ChatGPT nothing short of acting as your mentor in understanding the myriad of terminologies in data analysis. The best way to learn any skill is by asking questions and ChatGPT enables you to do the same. Having some level of interactivity always improves your learning capabilities and helps in understanding that particular domain inside-out.

ChatGPT's Code Interpreter's impact on Data Analysis

Exploring different solutions

Keeping aside the basic framework of data analysis, there isn’t a checklist defined that one can follow to find inference from the dataset. Data analysis and programming is a form of art. Art differs for each individual and can only be improved when you have explored other arts. With ChatGPT you can access different solutions with justifications that you may not have even thought of. With the addition of the Code Interpreter, ChatGPT has now additional context to work on, which improves the solutions drastically.

ChatGPT's Code Interpreter's impact on Data Analysis

Data visualization

This is hands down one of the best features of Code Interpreter (or ChatGPT Plus) currently — the ability to display visualizations and images. Visualizing your dataset makes the overall process of understanding the attributes a lot quicker. Extending our previous use-case of listing out the different methods to find outliers, we can graphically illustrate the same using box-plots and histograms.

ChatGPT's Code Interpreter's impact on Data Analysis

In the screenshot above, you can also see that the Code Interpreter self-debugged the error and generated the visualization for the outliers.

Understanding existing code

Reading through code can consume a lot of time especially when there is a lack of comments or the comments are insufficient. Using the Code Interpreter, you can simply upload the python or jupyter notebook file and ask ChatGPT to summarize the Code for you. You can also ask questions about the code. Although this was possible previously, it wasn’t as seamless and also had context limitations. This use-case can turn out to be really useful during training or collaboration.

ChatGPT's Code Interpreter's impact on Data Analysis

Will the Code Interpreter replace Data Analysts?

This is just the beginning of AI-based tools, and they will continue to improve with additional features and larger context-windows. The AI revolution is likely to replace many jobs, but it will also create twice as many jobs that we may not have even imagined yet. Tools like the Code Interpreter will handle tedious and redundant tasks, enabling Data Analysts to focus more on improving data quality and making more informed decisions. Additionally, ChatGPT will assist in enhancing the skills of existing Data Analysts and help them advance in their careers.

“AI won’t replace you. A person using AI will.”

In this AI era, it is crucial for businesses to have well-trained employees, and incorporating AI for employee training can be a significant investment. If you are seeking AI solutions to train your employees, Cody is the right tool for you. Similar to ChatGPT, Cody can be trained on your business data, team, processes, and clients, using your unique knowledge base.

With Cody, businesses can harness the power of AI to create a personalized and intelligent assistant that caters specifically to their needs, making it a promising addition to the world of AI-driven business solutions.

Subscribe to ChatGPT Plus and get access to the Code Interpreter along with a host of additional featured. Link to the Code Interpreter chat.