Hello everyone! I’m glad you’re here, and welcome to our webinar on “How to Build a GPT-Based System
From Idea to Deployment: From idea to deployment” My name is Shemmy, and I’m thrilled to be with you today.
Before we begin, I’d like to share a couple of important points with you. This webinar is being recorded, and rest assured, we will share a link to this recording with all participants after the session.
In addition, we will also share a copy of the presentation with you. This document will include all the links to the resources and references that will be mentioned throughout this webinar. This way, you don’t need to worry about taking notes or missing out on any important details.
So, let’s embark on this insightful journey together!
Agenda
Shortly about the agenda:
- Of course, for the new comers, I’ll introduce myself.
- Then short introduction to GPT
- How companies use GPT today with some practical examples,
- Choosing the best Large Language Model
- GPT Limitations You Must Know
- How to Determine Where GPT Can Enhance Your Business
- 10 Steps to Launching Your GPT Project
- How can we help?
- and Q&A, of course
1. About Me
So, first, let me introduce myself.
For those of you participating in a webinar for the first time, here’s a brief introduction: I’m Shemmy Majewski, CEO of DLabs.AI. When I was 10, I was diagnosed with diabetes, which made data an integral part of my daily life. This ignited my passion for Big Data and led me to establish a company focused on creating a mobile app for diabetes management using AI.
Now, at DLabs.AI, my team and I harness data to improve not only the lives of those with diabetes but also the lives and businesses of our global client base. We use AI and Machine Learning to help businesses improve workflows, drive efficiencies, and discover new AI-based business models.
2. Introduction to GPT: Key Terms & Numbers
Okay, enough about me; let’s delve into today’s webinar topic: GPT
While some of this might be straightforward for the technically savvy among you, I often find that people confuse two important terms: GPT and ChatGPT. Let’s clarify.
GPT, or Generative Pre-trained Transformer, is a type of AI model designed to generate text that is remarkably human-like and contextually relevant.
So, what is ChatGPT? It’s a specialized application of the GPT model that has been fine-tuned for generating conversational responses. It’s ideal for creating chatbots or virtual assistants.
In summary, GPT serves as the overarching model, while ChatGPT is a specialized implementation focused on conversation. Therefore, when you’re developing software and need to integrate conversational features, you would use the GPT model, not ChatGPT.
Without a doubt, ChatGPT has been a game-changer in the tech world last year. We’ve seen this firsthand at DLabs.AI, where interest in AI has skyrocketed, thanks in part to the array of capabilities ChatGPT brings to the table. From writing and coding to planning your shopping list and even written poems or crafting official letters—ChatGPT has a multitude of applications.
Now, let’s take a look at this chart. It indicates the time it took for various platforms to reach a user base of 100 million. As you can see, ChatGPT shattered second records, reaching this milestone in just two months. It was overtaken only by Instagram Threads, but it’s obvious that the app could benefit from promotion via Instagram and Facebook.
Quite an impressive feat, don’t you think?
Well, ChatGPT has not only sparked widespread conversations across platforms like LinkedIn but has also been instrumental in cultivating a new generation of AI experts.
But shifting our focus to the business landscape, numerous companies have begun to integrate GPT technology into their operations. This demonstrates not only their commitment to staying ahead in technological advancements but also positions them with a competitive edge in the market.
3. How Companies Use GPT: Practical examples
You’re probably wondering, “Okay, enough theory—show me some real-world examples, right?”
Well, let’s dive into some research and case studies to really get what this tech can do. Who knows, it might even spark some cool ideas for your own projects!
As you already know, I have a special interest in the healthcare sector, so let’s kick things off with a study that recently caught my eye. The study is titled ‘Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum.’
The research team took 195 real-world patient questions from a social media forum and got responses from both actual physicians and a chatbot. Then a panel of healthcare professionals assessed these responses on two critical parameters—quality and empathy.
Now, here’s where it gets interesting. Nearly 79% of the evaluations favored the chatbot’s answers over the human physicians. Yep, you heard that right! And it wasn’t just about the content. The chatbot’s responses were longer and rated as more empathetic compared to their human counterparts.
These findings open up a wealth of opportunities for how we envision the role of AI in healthcare. It’s not merely a tool for data crunching or administrative tasks; it could genuinely help in patient interaction, even outdoing humans in some respects.
Alright, let’s shift gears a bit and look at how companies in the healthcare sector are actually applying GPT technology in their operations. And we’re not just talking about studies and theories here; we have concrete examples.
Take Nabla, a Paris-based digital health startup co-founded by AI entrepreneur Alexandre Lebrun. Nabla claims to be one of the first to deploy GPT-3 in a tool specifically designed to assist physicians, and it’s all about reducing paperwork. They’ve named this service ‚Copilot.’
Launched as a Chrome extension, Copilot essentially acts as a digital assistant for doctors.
As doctors conduct patient consultations, Copilot translates those interactions into various types of documentation, such as prescriptions, follow-up appointment letters, or consultation summaries. And yes, it’s all powered by GPT-3.
Nabla was an early adopter of GPT-3 back in 2020 and is currently using it as a base for Copilot. But what’s more intriguing is their long-term vision. Alexandre Lebrun admitted they’re planning to build their own specialized language model tailored for the healthcare industry.
Even though it’s an early version, Copilot has already gained traction. It’s being used by practitioners not just in France but also in the U.S., and has been adopted by about 20 digital and in-person clinics with sizable medical teams.
Continuing on the theme of GPT’s diverse applications, let’s take a closer look at Amazfit’s groundbreaking advancement in smartwatch technology. They’ve unveiled the world’s first watch face integrated with ChatGPT.
This integration allows the watch face to display human-like, AI-generated responses. Once activated, the watch face can ask how your day has been or offer a greeting. Additionally, it can display health and fitness data such as step count, calories burned, and more. Users can also customize it to show information like battery percentage and heart rate.
Moreover, the watch comes with voice recognition features, enabling you to interact with ChatGPT simply by speaking to your watch. Now, how convenient is that?
But the innovation doesn’t stop there. Not only does this watch face facilitate human-machine interaction through ChatGPT, but it was also developed using ChatGPT’s code-generation capabilities. That’s right—the AI played a role in its own programming!
So, as you can see, as AI and GPT models continue to evolve, their integration into everyday devices like smartwatches isn’t just on the horizon—it’s already here.
Now let’s talk about a standout example from the EdTech industry, one of my favorites, Duolingo. They’ve harnessed the capabilities of GPT-4 to offer an incredibly personalized and interactive learning experience.
With Duolingo Max, users can take advantage of “Explain My Answer” and “Roleplay” features. Imagine having a 24/7 language tutor that provides in-depth feedback and engages you in dialogues; that’s what these features offer.
Duolingo played it safe, limiting these AI-enhanced features to just two languages initially. This approach likely helps them manage system loads and stay within GPT’s token limitations, preventing them from overburdening their infrastructure.
And here’s why this example stands out for me: these advanced AI-based features come as part of a new subscription tier. This perfectly illustrates how AI isn’t just enhancing user experience, but also helps to create new business models and opens up fresh revenue streams.
OK, let’s shift our focus from learning to some pleasure. Night Shift Brewing, a brewery based in Everett, Massachusetts, was co-founded by three roommates.
The team used ChatGPT to develop a new beer recipe. Despite initial reservations about public perception—given that brewing is traditionally a hands-on craft—the results were nothing short of astonishing. With a few adjustments, a new beer called “AI-P-A” was created. The team even utilized AI to design the label and name for this groundbreaking brew.
What’s even more compelling is the public’s reaction. Night Shift launched the new beer as a limited run, unsure of what to expect. To their delight, it turned out to be a hit, drawing in customers specifically looking to try it.
So, we explored fascinating examples of how GPT is changing industries around the globe. Now, I’d like to bring it back home and share what we’re working on here at DLabs.AI, specifically in the healthcare sector.
One of the most pressing health concerns in Poland right now is the alarming rise in diabetes diagnoses. The healthcare system isn’t sufficiently educating newly diagnosed patients or their families, leaving them overwhelmed and unsure how to manage their condition effectively.
To address this, we’ve developed SugarAssist, an LLM-powered diabetes assistant. This comprehensive chatbot is designed to help patients and their families navigate the complexities of diabetes management. SugarAssist covers essential areas from Knowledge Assistance, where it offers tailored guidance on blood sugar monitoring and medication management, to Sports Advisory that advises how physical activities could impact one’s condition.
We’ve also integrated Emotional Support features to help individuals cope with the new diagnosis emotionally.
The chatbot is not only personalized but also ensures the user’s safety by delivering only verified advice. This makes it a reliable and invaluable tool in managing this chronic condition.
Honestly, if this tool had been available two decades ago, it would have been a game-changer for me. I would have relished the opportunity to use it.
Our next project tackles the challenges of LinkedIn outreach with DLeads.AI. We’re all aware that LinkedIn has become a crowded marketplace where businesses are in constant competition for attention, making it tough to break through the noise.
Our project aims to resolve two main challenges. The first is capturing attention amidst a deluge of generic messages. The second is balancing personalization with scalability in our outreach.
That’s why we developed DLeads.AI, a tool that merges GPT technology with user-specific data to produce highly targeted messaging sequences. But these aren’t run-of-the-mill sequences; they are meticulously customized based on the sender’s profile, the recipient’s characteristics, and a clearly defined problem-solution framework.
If you’re intrigued and wish to explore DLeads.AI, we’re offering free trials. You can either scan the QR code displayed on this slide.
Let’s move on to our next project, this time for the edtech industry.
Higher education has become an increasingly competitive field, leaving students overwhelmed with options. The big question here is: how do you match the right courses and colleges with individual aspirations and academic profiles, while providing timely and accurate information?
To tackle this, we’ve developed an AI-powered Student Assistant. This isn’t just a chatbot that answers queries; it’s an academic advisor that understands each student’s unique needs and aspirations. It scans our exhaustive internal database to suggest the most fitting educational paths.
So what sets our solution apart?
Firstly, it offers a rich information source, delving deep into curriculum details, entry prerequisites, and faculty qualifications.
Secondly, it maintains fluid, human-like interaction, addressing the need for immediate and responsive communication.
Thirdly, the AI agent provides personalized educational guidance tailored to individual skills and aspirations. Lastly, the system is trustworthy, transparently communicating its limitations when faced with unknown queries.
Next, let’s look at how we’re helping Boldly, a premium remote staffing company. Their challenge lies in the significant amount of time their assistants spend on organizing meetings for C-level executives. Add to that the complexity of context-switching among multiple clients and meetings.
To solve this, we decided to use Large Language Models to analyze email threads, thereby offering real-time updates on the current status of meeting arrangements.
We created a software that auto-generates meeting records, detects key actions in emails, and provides a bullet-point history for quick referencing.
So, what are the key benefits? Our tool substantially reduces the time assistants spend on tasks and provides robust support in meeting organizations.
4. Choosing the Best LLM: Key Selection Criteria to Consider
Now that we’ve explored various real-world applications of GPT in different companies, you’re probably wondering how you can get started on your own project.
First and foremost, it’s crucial to understand that while GPT is powerful and widely used, it’s just one among many language models available.
Other options include BARD, LlaMA, Falcon, Cohere, PaLM, and Claude v1, each with its own set of features and capabilities. Furthermore, even if you decide to go with GPT, you have choices like GPT-3.5 and the newer GPT-4.
The question then becomes: How do you make the right choice? So, let’s delve into tips and strategies to guide you in selecting the best language model for your specific needs.
First, consider the quality criteria. Your chosen model should be effective in solving your specific problem. For example, while GPT-3.5 is powerful, it may not be enough for certain tasks, and you may need to upgrade to GPT-4.
Second, keep an eye on the cost. Naturally, smaller models will be more budget-friendly. Aim to select the smallest model that is still capable of effectively resolving your specific problem.
When choosing a language model for your project, you must consider the trade-offs between open-source and proprietary models.
Open-source models like LlaMA are great if you want total control—over the version, your own instance for privacy and security, and even the hardware you run it on. It also provides a way to free yourself from reliance on large corporations.
On the other hand, if you’re developing an enterprise application requiring robust Service Level Agreements (SLAs), GPT or models hosted on platforms like Azure might be better. These often come with the benefits of a managed infrastructure and continuous support and updates.
A third pathway is using open-source models in a pay-as-you-go scheme, such as through Huggingface’s interface endpoints. Here, you don’t build the infrastructure from scratch but select the model you want to use. It’s a good middle ground in terms of cost and flexibility.
Finally, consider the length of input data. When working with projects requiring the analysis of large volumes of text, the input length becomes a crucial factor to consider. Here, you have two approaches:
The first approach involves using models like GPT-4 Turbo, which supports a context window of up to 128,000 tokens, or Anthropic that supports up to 200,000 tokens. These models are built to handle long stretches of text and make inferences based on that. So, if your project requires deep insights from comprehensive documents, these models are particularly suited.
The second approach focuses on filtering the data before feeding it to the model. If your task doesn’t require the whole document for generating meaningful output, then you can use models with a smaller context window. But be cautious—this approach requires a robust mechanism for extracting only the essential pieces of information from the text. Any oversight could lead to incorrect or inadequate conclusions.
When going for the pre-filtering route, it’s crucial to implement an effective data extraction system that can smartly pull out relevant snippets from the larger dataset. Poorly designed systems might omit crucial details or include irrelevant ones, affecting the model’s performance.
5. GPT Limitations You Must Know
So, as today’s webinar centers around GPT, let’s focus on this model. To make an informed decision about integrating GPT into your business processes, a clear understanding of its limitations is essential. Well, let’s delve into these constraints.
One major issue with ChatGPT is that it can lose the context of conversations. Even though it learns from our interactions, it might feel like talking to a forgetful grandparent who can’t remember what was said in the last sentence.
How to address this problem? We need to train the model in a way that helps it retain context.
Preparing training data for ChatGPT can be pretty time-consuming. The input data required depends on what you want to train the model for, and it’s essential to keep text length limitations in mind. Additionally, the inference process can be more complicated than just summarizing data, as it requires data and logic.
Using external tools like OpenAI involves potential risks such as price increases, making contingency plans vital. In certain instances, building your own models may be the best solution.
Another aspect to be aware of is service downtime. Since we don’t have control over external services like OpenAI, contingency plans are crucial. Currently, OpenAI doesn’t offer a guaranteed Service Level Agreement (SLA), although it is mentioned on their official site that they are actively working on publishing them and are committed to achieving this.
Therefore, you need to be prepared for possible service interruptions.
It’s also essential to recognize that ChatGPT can sometimes fabricate information. You cannot trust all generated content, as the model may provide unrealistic or incorrect answers.
Validating the accuracy of large language models can be pretty challenging, so it’s crucial to approach their outputs cautiously.
I’ve noticed that many companies are hesitant to implement GPT or AI at all, due to concerns about data security. Indeed, sending confidential information to a third-party API always carries the risk of violating personal privacy or inadvertently exposing sensitive data.
I’ve noticed that many companies are hesitant to implement GPT or AI at all, due to concerns about data security. Indeed, sending confidential information to a third-party API always carries the risk of violating personal privacy or inadvertently exposing sensitive data.
Let’s take Samsung as a case study. The global tech leader had to enforce a ban on ChatGPT when it was discovered that employees had unintentionally revealed sensitive information to the chatbot.
According to a Bloomberg report, proprietary source code had been shared with ChatGPT to check for errors, and the AI system was used to summarize meeting notes. This event underscored the risks of sharing personal and professional information with AI systems.
It served as a potent reminder for all organizations venturing into the AI domain about the paramount importance of solid data protection strategies.
Fortunately, you can significantly mitigate privacy risks, for example, through data anonymization. This technique involves removing or modifying personally identifiable information to produce anonymized data that cannot be linked to any specific individual.
However, this is just part of a holistic approach that should also include data encryption, strict access controls, and regular audits. By adopting these combined strategies, organizations can more safely navigate the complex landscape of AI and data privacy.
Have you already heard about prompt injection? This technique allows an external entity to control the language model’s output by manipulating the input prompt. This issue often arises when untrusted text becomes part of the model’s prompt. It poses a particular problem for businesses that use language models to interact with the public, as it can compromise both the integrity and reliability of the model.
An interesting case is that of Remoteli.io, which created a language model to respond to Twitter posts about remote work. However, Twitter users quickly figured out that they could manipulate the bot’s responses by injecting their own text into their tweets.
The reason this works is that remoteli.io takes a user’s tweet and concatenates it with their own prompt to form the final prompt that they pass into a LLM. This means that any text the Twitter user injects into their tweet will be passed into the LLM.
This vulnerability allows for the potential misuse of the model in ways that can be harmful, such as spreading misinformation or inappropriate content.
If you’re using a GPT model for public interactions or content generation, it’s crucial to be aware of this limitation and take steps to mitigate it, such as validating and sanitizing the input prompts.
Another factor we should consider when working with GPT is the response time of the APIs. Our experiments with various large language model (LLM) apps revealed significant latency issues in some cases. OpenAI’s documentation clarifies that the speed of the response largely depends on the number of output tokens generated by the model. While these numbers are subject to change as OpenAI updates its infrastructure, it’s essential to note that latency may also vary due to total load and other unpredictable factors.
One practical tip for optimizing GPT API response times is to minimize the number of output tokens generated.
6. How to Determine Where GPT Can Enhance Your Business
Drawing from countless AI consultations, I’ve picked up on a trend: many business owners recognize the promise of AI and LLMs but are in the dark about its practical application in their business. They often approach me with a few scattered ideas, hoping to carve out a clearer path.
So the real challenge is figuring out where AI fit in your business puzzle.
While the allure of AI is undeniable, I must admit that it’s impossible for me to fully step into the shoes of a business owner and magically discern their unique needs. Sure, there are specialized AI tools tailored for certain industries, but if a challenge is widespread, there’s a good chance a solution already exists out there.
So, the real conundrum? Zeroing in on where AI can truly make a difference in a business. Here’s a thought: Rather than merely beefing up your staff, why not consider how tech could let you cater to more clients without a proportional hike in operational costs? Now, diving headfirst into AI might seem like the next logical step, but it’s crucial to tread with purpose.
Before hopping on the AI hype train, businesses should pin down their specific challenges and gauge whether AI is the answer.
I can pitch a few AI-centric ideas, sure, but the crux of the matter remains: where are the bottlenecks in your operation? Are there tasks that are tedious or just plain repetitive? Those are your prime candidates for automation.
Navigating the AI space sometimes means embracing a spirit of experimentation. Those ready to test the waters might just strike gold. Take Duolingo, for instance. They rolled out a fresh subscription tier powered by GPT, giving users real-time feedback and simulated conversations (which, by the way, is a testament to how AI can spawn innovative business models and usher in fresh revenue streams).
Interestingly, Duolingo played it safe, limiting their AI feature to just two languages initially. This cautious move likely stems from the risk of overburdening their system. Overstepping usage limits (or, in the case of GPT – so-called tokens) can be all too easy, which might shed light on their conservative rollout.
It’s a textbook lesson in how businesses can blend vision with caution when integrating AI into their offerings.
Embarking on the AI journey can be overwhelming, especially if you’re new to the field.
At DLabs.Ai, we recognize the importance of a gradual, informed approach to AI integration. That’s why we offer a variety of services designed to help you navigate these waters with confidence.
Our range of services, from initial workshops to comprehensive audits, are designed to explore AI’s potential for your specific needs without immediate commitment to full-scale implementation.
These standalone services enable you to assess AI’s impact and practical application, helping to identify the best areas for implementation, calculate ROI, and build prototypes.
So, whether you’re a startup curious about AI’s potential or an established company looking to integrate AI solutions, our aim is to guide you through this journey. We provide the insights and tools necessary to make informed decisions, experience the benefits of AI, and navigate your path towards AI adoption with absolute confidence.
So, if navigating AI seems daunting, expert support can be a game-changer.
If you’re not ready for dedicated AI services, a great initial step is staying informed about market trends. By being here at this webinar, you’ve already begun that journey – congratulations on this proactive move!
Staying abreast of the AI world is crucial. I recommend regularly reading industry reports and market predictions to keep up with the latest developments.
Another effective way is to follow industry newsletters focused on AI. At Dlabs.AI, we offer two valuable resources:
The first one is InsideAI – email newsletter, where we share monthly updates on AI news, tools, resources, events, open source projects, and more.
I also run ‘Bits and Bytes‘, a newsletter where I offer my insights on AI implementation, along with subjective commentary on select AI news. It’s a more personalized take on the AI world, offering practical tips and perspectives. If you haven’t subscribed to these yet, I highly encourage you to do so.
This way, you’ll be well-informed and better prepared when you decide to take the next step in your AI journey.
In the fast-paced world of AI, general news or broad tips might not suffice for businesses seeking a more focused approach. Recognizing this need, we’ve developed AI Radar at DLabs.AI, a service tailored for companies that want to stay informed and prepared but aren’t yet committed to a specific AI solution.
It offers expert guidance and in-depth analysis, providing your team with unseen perspectives that inform better business decisions.
With AI Radar, you’ll receive AI-enhanced forecasts, helping you anticipate market trends and stay ahead of the competition.
We also provide sector-specific news and carefully selected case studies, ensuring the information is directly applicable to your unique business area.
If it sounds interesting, scan the QR code (or click here). to read more.
7. 10 Steps to Launching Your GPT Project
Alright, let’s dive into the hands-on stuff—how to actually build an app using GPT.
As I said earlier, my team and I have made some apps using GPT. One of the best examples is our GPT-based Student Agent.
Just to refresh your memory, the Student Agent serves as a specialized personal assistant.
It uses our data to answer questions about schools, courses, and what you might want to study.
Beyond simply answering questions, it’s designed to guide the conversation strategically, thereby generating personalized course recommendations for each user.
Our journey in creating any AI-based project for that matter, begins with a critical first step: Data Gathering. This phase serves as the cornerstone for everything that follows.
In our specific case, we needed to gather a rich set of data about universities, courses, and faculties. But no matter what your project focuses on, remember: the quality of your model’s output will be heavily influenced by the quality of the data you provide.
It’s not just about having lots of data; it’s about having the right data. And it needs to be both comprehensive and detailed. This will set the stage for a more effective and reliable GPT application.
After you’ve gathered your data, the next critical stage is Data Cleaning.
Large datasets often contain errors, inconsistencies, or redundant information. These can adversely affect the performance of your GPT model and the accuracy of its outputs. Therefore, it’s essential to go through the data meticulously and eliminate any inaccuracies or redundancies.
By taking the time to clean your data, you’re not just setting up for a smoother implementation phase but also ensuring that your model interacts only with relevant, accurate data. This will significantly improve the model’s performance and the reliability of its outputs.
After cleaning the data, the next thing we focus on is organizing it in a way that’s most useful for machine learning models. We need to transform the raw data into a structured format that can be readily used for analysis. This is key to making the data compatible with our internal systems and machine learning algorithms.
For simpler projects, a CSV file might do the trick, but for more complex datasets, you may need to look at more advanced data storage and processing formats.
One of the significant challenges at this stage is handling sensitive data like medical information. This is where aforementioned data anonymization comes into play. To protect personal information, for example, we might replace real names with generic identifiers like “Person 1,” “Person 2,” etc. By doing so, we’re making sure the data is not only optimized but also secure.
Remember when I mentioned the importance of choosing the right Large Language Model (LLM) for your project? Well, here we are, and the decision is crucial for several good reasons.
The landscape of LLMs is continuously evolving. New models are being developed regularly, some of which may be better suited for your specific project. For instance, Llama 2, an open-source model, demonstrates potential, rivaling the capabilities of GPT-3.5. It’s important to stay current and adaptable in this dynamic field.
For our project, we didn’t limit our search to just one provider. We explored multiple options, including some of the newest models to hit the market.
Ultimately, we chose OpenAI. Then came the decision between different versions of GPT. You might be surprised to learn that we opted for GPT-3.5 Turbo over the newer GPT-4. So, why did we do that?
Several factors came into play, including token limits, pricing, and the quality of the responses. GPT-3.5 Turbo met our specific needs more effectively.
It’s crucial to remember that each model has its own set of strengths and weaknesses. You need to weigh what’s most important for your project. In our case, we prioritized performance and cost, aiming for a model that would deliver solid results without breaking the bank.
If you aim for your app to leverage your knowledge base for responses rather than drawing solely on the model’s pre-trained data, you have 2 key strategies to consider.
The first one is fine-tuning. In short, it is a process where you adjust certain representations of a pre-trained model to align more closely with the problem at hand.
It’s like sharpening a knife: the model, initially broad in knowledge, is honed for a specific domain through additional training on a task-specific dataset.
For example, fine-tuning GPT on medical texts transforms it from a general language model to a specialist in medical diagnoses.
Interestingly, data training is exactly how GPT learns.
However, OpenAI cautions against using this method for knowledge transfer, suggesting it’s more apt for specialized tasks or styles, not as the primary instructional method for models.
Data embedding is the method recommended by OpenAI, involving allocating unique numerical identifiers to data chunks. What does that mean?
Well, imagine a basic map displaying various landscapes like rivers, forests, and sea.
If tasked with placing a tree icon on this map, intuitively, you’d settle it within the forest.
Similarly, inquire about a ship, and the system (recognizing the semantic connections) may reference data related to the ‘sea’ on its internal ‘map.’
At its core, embedding establishes a semantic connection between questions and answers, mirroring how you’d organize related items on a map.
Simply put, embeddings place each word as a point on a coordinate system. Words with similar meanings are located close to each other. For example, the word “apple” would be located close to the word “pear” as they’re both fruits but far from the word „London”.
So, why did we go with Data Embedding instead of Fine-tuning? Trust me, this wasn’t just a coin flip; there were concrete reasons.
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First up, Ease of Implementation. With Data Embedding, we didn’t have to go back and retrain the model. That’s a huge time-saver and also keeps our resources intact.
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Second, Effectiveness. Imagine you’re asking the system a question, but you don’t use the exact technical term. Data Embedding is smart enough to understand the context and still give you an accurate answer.
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Moving on to Efficiency, this method is not just fast; it’s also cost-effective. We didn’t have to spend extra on data search credits.
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Next, Compatibility. Our system already uses semantic search, and Data Embedding works perfectly with it. It ensures that we’re always hitting the mark with our responses.
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Now, what about Adaptability? This method lets us get to know what our users are really after. It’s not just about answering their questions; it’s about understanding them and offering tailored recommendations.
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Lastly, Versatility. Data Embedding isn’t just for text. It can also be applied to images. Imagine the potential! The ability to correlate text with images brought our project to a whole new level.
So, to wrap it up, Data Embedding wasn’t just a choice; it was the clear winner for us. It offered a robust and versatile way to link our chatbot to our rich knowledge database.
Let’s move on to Contextual Prompts. Unlike traditional chatbots that just react to questions, ours takes the initiative. How do we do it? We’ve built in what we call ‘contextual prompts.’
These prompts are coded directly into the chatbot and work invisibly, meaning the user isn’t aware of them. As people interact with our chatbot, these prompts subtly steer the conversation in specific directions.
Why do we use them? In the case of our Student Agent, the goal is simple yet crucial: To guide users to the most fitting university and course based on their responses.
So, by leveraging these invisible guiding forces, we’ve made the chat experience feel more like a flowing conversation and less like talking to a machine. The result is a system that’s responsive to each user’s unique needs.
Let’s now talk about Semantic Search, which is Step 7 in our journey. Do you remember the concept of Data Embedding? Well, Semantic Search takes that a step further. It’s not just about recognizing the words a user says; it’s about understanding what those words actually mean in context.
For our agent, the foremost task is to deduce the overarching fields of study from the user’s statements. This deduction forms the groundwork for our semantic search. Following this, we derive embeddings based on this deduction to identify the most appropriate universities and courses. Upon gathering this pertinent information, we channel it as input to the AI model.
Now we’re moving on to Step 8: Model Testing and Refining.
You need to be aware that chatbot’s initial deployment isn’t the final stage but merely the beginning of an ongoing optimization process.
For our student agent, we subjected it to a stringent testing regimen. It wasn’t just to ascertain its performance and identify anomalies like hallucinations or incorrect responses. Uncovering these issues is the first step; addressing them is the true challenge, and this is where prompt engineering comes into play.
It is where you iteratively adjust and fine-tune your prompts to mitigate undesired behaviors and improve the chatbot’s accuracy. Think of it as training a student; you must correct mistakes repeatedly until they consistently produce the right results.
The next step is User Interface Creation. You could have the most intelligent chatbot ever, but if interacting with it feels like a chore, then what’s the point? This is why developing a user-friendly interface is so crucial.
For those of us who are knee-deep in data and AI, the temptation to tackle UI design ourselves might be strong, but the smart move here is to bring in the professionals. If you’re not a UI/UX expert, it’s a good idea to collaborate with someone who is. Trust me, your users will thank you.
And now, the final step, Step 10: Live Environment Integration. You’ve got a refined model and a brilliant interface, and you’re ready to take it live. But don’t forget that this isn’t just about launching a standalone chatbot; it’s about how that chatbot fits into your broader business ecosystem.
For our project, the initial prototype was developed in just two weeks. However, inserting it into our existing workflow and platforms wasn’t quite as speedy. This process demanded rigorous testing and tight coordination between our front-end and back-end teams to ensure a smooth integration.
So, while it’s thrilling to go from prototype to live product, integrating it into a broader, more complex system presents its own set of challenges. That’s why it’s critical to plan carefully, foster inter-team collaboration, and remain adaptable as you roll out the new system.
8. How can we help you?
Every problem and challenge is different and must be treated individually. Fortunately, my team and I know very well how to avoid all the risks associated with AI implementation. So, how can we help?
If you’re looking to implement GPT or other AI technologies in your business but aren’t sure where to begin, that’s where we come in.
At DLabs.AI, we recognize that each business has its unique set of challenges and goals. That’s why we don’t offer a one-size-fits-all path for each company. During a free initial consultation, which I personally host, I strive to understand your needs. Then, together with my team, we tailor our services to fit your specific business case.
Whether it involves crafting a custom AI strategy, setting new AI-KPIs, developing machine learning models, establishing your data infrastructure, or integrating large language models into your systems, we always try to adapt our approach to match your needs.
Think of us as your adaptable partner in AI, ready to guide you from the initial idea to successful implementation. We believe this individualized approach is key to ensuring your success and delivering measurable, impactful results.
