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.
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.
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:
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.
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.
Prompt:
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
Settings
Field Type
Knowledge
Percentage of tokens used to provide knowledge base context.
65%
Chat History
Percentage of tokens used to provide chat history.
10%
Response
Percentage of tokens allocated for the AI’s generated response.
25%
Reverse Vector Search
Improve relevance in knowledge search by merging AI and user responses.
Off
Persistent Prompt
Maintain AI compliance by continuously re-emphasizing the prompt.
On
Relevance Score
Trade-off between fewer knowledge base contexts for higher accuracy and response completeness.
Wide
Model Used
GPT-4
Step 2: Enable the Zapier Integration
To enable the Zapier Integration, go to Account > Integrations and install Zapier.
After clicking Install, you’ll be redirected to Zapier, where you’ll need to accept the 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.
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.
You’ll encounter two key events in the setup:
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.
Action: This follows the Trigger and executes specific tasks like sending a message or adding data to a table.
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!
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:
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.
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.
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.
Create a new chat and select GPT-4 with 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:
Understanding your dataset
Handling missing/invalid values
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.
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.
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.
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.
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.
After announcing a temporary ban on ChatGPT following its launch, StackOverflow has now decided to jump on the GenAI bandwagon with their latest offering, OverflowAI. OverflowAI is not a single product but a collection of multiple GenAI products under one umbrella term. Let’s see if OverflowAI is really a ChatGPT replacement for programmers.
What’s so special about OverflowAI?
Search
To improve and save time in searching for solutions to questions, OverflowAI will aggregate knowledge from various sources to stitch a step-wise solution catered to solving your specific problem. All the resources used to generate the response will be cited with references so that you can validate the answers yourself, and credits will be given to the contributors of the solution.
Follow-up questions can be asked in a chat-like format. This will maintain the context of the original question and add more information onto it, allowing you to spend less time on structuring the question and ask a series of questions that are linked to one another.
Draft
“AI isn’t replacing humans anytime soon, but it can help you draft a question to post to our community” – Prashanth Chandrasekar, CEO @ StackOverflow
There have been instances where most questions are not solved or ignored, purely due to the lack of structure or redundancy of information within the question. OverflowAI can help you draft better questions that can be posted on the StackOverflow community, which can then be answered by domain experts.
The same feature is used when OverflowAI is unable to answer a particular question. Instead of hallucinating answers, it will simply prompt the user to redirect the question to the community and also provide the user with a well-drafted question.
Summarize
If you are a developer, you definitely know the pain behind reading and skimming through multiple responses and documentation to find a solution to one simple problem. OverflowAI, with its GenAI solution, summarizes multiple responses and discards redundant or less useful responses to provide you with a clean and well-structured summary of the solution to your problem.
These attributed and trusted answers can be refined based on coding ability, length, and other knowledge bases such as GitHub. With StackOverflow for Teams, you can also refer to solutions provided by colleagues from your enterprise by training OverflowAI on your repos.
Plugins
“One of the challenges we hear from developers is minimizing disruption and context switching while coding” – Prashanth Chandrasekar, CEO @ StackOverflow
The plugin for Visual Studio Code is designed to act like a pair-programmer, helping you improve your programming efficiency by providing you with validated and attributed content from public and private StackOverflow teams. This extension imports verified content from your private Stack Overflow for Teams instance and the public platform to give your developers a personalized summary of how to solve their issues quickly and effectively, allowing them to delve deeper where necessary and then document new insights and solutions.
Slack Integration
Since most companies rely on Slack as their primary medium of communication now, the Slack Integration for StackOverflow will make information accessible to everyone easily, and solutions can be found collaboratively on channels. All teams can interact with the resources and knowledge base without any human assistance.
How is it different from ChatGPT?
With the myriad of LLMs currently out there, not all of them can stand out based on their LLM capabilities. ChatGPT is a tool that is created to showcase the power of GPT models in everyday usage. Tools like OverflowAI are specialized to be used for specific use-cases, in this case, software development and maintainability. Yes, you can use ChatGPT to get most of your work done, but specialized tools help in reducing your workload by making the entire process a lot more seamless and robust.
If you are looking for a tool like OverflowAI but for your business and be trained on your business documentation, let us introduce you to Cody. Much like OverflowAI, 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.
To try OverflowAI, you will need to register on StackOverflow Labs as it is still in the experimental phase.
A couple of days ago, Meta released its latest version of LLM called Llama 2 in collaboration with Microsoft. If you have been following the LLM hype, you might have already heard about it or even read about its new features. To simplify things, we will list down four reasons why Llama 2 is generating so much hype and how it compares with some of the best LLMs.
Free for Research and Commercial Use
One significant reason that has caught people’s interest in Llama 2 is that Meta made the entire model free for almost everyone, except for some big enterprises that may have certain conditions. This move opens up exciting opportunities for individuals thinking of starting their own businesses or venturing into the world of Generative AI. Now is the perfect time to dive into the waters of AI, especially with a language model of this caliber being freely accessible. While there were already multiple open-source models available, none of them came from a company of Meta’s stature and could serve as direct competitors to GPT.
“There have been public releases of pretrained LLMs (such as BLOOM (Scao et al., 2022), LLaMa-1 (Touvron et al., 2023), and Falcon (Penedo et al., 2023)) that match the performance of closed pretrained competitors like GPT-3 (Brown et al., 2020) and Chinchilla (Hoffmann et al., 2022), but none of these models are suitable substitutes for closed “product” LLMs, such as ChatGPT, BARD, and Claude.” — Meta Research Paper
Safety
Based on the reports published in the Meta research paper, Llama 2 has demonstrated superior performance compared to other open-source models in the helpfulness and safety benchmark. It has even outperformed ChatGPT (7b, 13b, 70b models) in these aspects. However, it is important to note that the research paper acknowledges the possibility of biased data favoring Llama 2, which should be taken into consideration while interpreting the results. Nevertheless, even if Llama 2 comes close to the ChatGPT benchmark, it deserves commendation.
One of the most significant factors contributing to the safety of Llama 2 is its data privacy. Unlike some models, Llama 2 does not require sending your data to an external server, such as OpenAI, to fetch responses. This unique attribute makes the model particularly valuable for critical and sensitive use-cases, as it helps safeguard users’ data and maintain their privacy. Users can run the model on private servers with their data being contained within their infrastructure.
Open Source
The most popular LLMs currently in use operate as black boxes, with users having limited insight into their functioning. In contrast, open-source models provide a transparent approach, allowing users to understand their inner workings. This transparency instills confidence and assurance when using such models, despite the challenges they may face, such as generating spam or disinformation.
Additionally, the open-source nature of these models encourages collaborative efforts, leading to continuous improvement and development in the field of LLMs. As a result, open-source models play a crucial role in driving advancements in the world of language models.
“And we believe it’s safer. Opening access to today’s AI models means a generation of developers and researchers can stress test them, identifying and solving problems fast, as a community. By seeing how these tools are used by others, our own teams can learn from them, improve those tools, and fix vulnerabilities.” — Meta Website
Although Llama 2 is openly licensed, Meta has still not disclosed the data it has been trained on, which still sticks out in terms of data privacy of Meta users. Meta says it “made an effort to remove data from certain sites known to contain a high volume of personal information about private individuals” in the Llama 2 research paper, but it did not list what those sites are.
Performance
Llama 2 is available in four different weights: 7B, 13B, 34B, and 70B. The weight represents the number of parameters the model is trained on. Generally, larger parameter sizes result in more accurate and reliable responses, but they also require greater computational resources. To improve the human-like characteristics of the model, Llama 2 undergoes fine-tuning using instruction-tuning and the RLHF (Reinforcement Learning with Human Feedback) method which is also used by GPT.
While the 70B parameter size is substantial, it still falls short compared to GPT-3.5, which has 175B parameter-size. As a result, Llama 2’s performance may not match that of GPT-3.5, but benchmark tests indicate a close competition even with its smaller parameter size. Despite this difference, Llama 2 outperforms all existing open-source models currently available.
“RLHF is a model training procedure that is applied to a fine-tuned language model to further align model behavior with human preferences and instruction following. We collect data that represents empirically sampled human preferences, whereby human annotators select which of two model outputs they prefer. This human feedback is subsequently used to train a reward model, which learns patterns in the preferences of the human annotators and can then automate preference decisions.” — Meta Research Paper
Conclusion
There is indeed a multitude of open-source models emerging, and with the release of Llama 2, the possibilities seem limitless. While it may take some time for these open-source models to directly compete with something as advanced as GPT-4, the excitement lies in getting a model that comes close to the capabilities of GPT-3.5. This progress in itself is truly remarkable.
Looking ahead, as LLM training becomes more efficient, the potential for having a personalized ChatGPT, fine-tuned with your data on your local device, becomes a tantalizing prospect. One platform that offers such capabilities is Cody, an intelligent AI assistant tailored to support businesses in various aspects. Much like 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.
Click here to read the Meta Research Paper on Llama 2. Try Llama 2 here.
