ChatGPT basics

Capabilities, Applications, and Considerations

Table of contents:

• Capabilities of ChatGPT for Business

• Using ChatGPT in Daily Life

• Limitations

  • Search Engines vs Large Language Models

• Introduction to prompt design and prompt engineering

• How does it work?

  • How is it so different from previous ChatBots?

  • Attention is all you need!

• Alternatives

• Legal and Ethical concerns

ChatGPT: bridging the gap between human creativity and AI efficiency

We all know what ChatGPT is, an AI-based tool designed to understand and generate human-like text based on instructions it receives, with the aim of augmenting human capabilities in various tasks, such as customer service, content creation, programming, improving productivity in different fields, and fostering innovation by allowing users to explore new ways of creating with AI, offering benefits such as high-quality text generation, versatility in applications, time-saving for repetitive tasks.

But... what else is there to know?

Capabilities of ChatGPT for Business

Imagine having a highly skilled assistant who is always ready to help with your writing tasks, research, and even coding questions, without getting tired. That’s what ChatGPT offers to your business.

• Writing and Email Assistance: ChatGPT can help you draft a professional and polite message in no time, chosing the tone you want, and even when you are not sure about what you want to say.

• Proofreading and Editing: Enhancing written materials. ChatGPT can also play the role of a second pair of eyes, helping you spot errors or suggesting better ways to phrase your sentences, making your messages clearer and more impactful. It considers aspects such as:

  • Clarity and Precision

  • Conciseness

  • Grammar and Syntax

  • Style and Tone

  • Engagement and Interest

  • Coherence and Cohesion

• Surveys and Quizzes Generation.

• Market Research: Summarizing findings, trends, articles, reports, and social media buzz, giving you a concise overview of your industry landscape.

• Translations: Multilingual support for global reach.

• Product Descriptions: Crafting compelling copy.

• Coding: Basic coding help and code review.

• Automating customer service inquiries.

• Generating reports and business insights, research competitors, seek help in deciding whether or not to invest in a company.

• Additional Uses: Encourage imagination: “The possibilities are nearly endless. Whether you need help organizing information, generating ideas for a marketing campaign, or even learning something new, ChatGPT is here to assist.”

Using ChatGPT in Daily Life.

Ever wished for a personal assistant to help with daily tasks, from planning meals to learning a new hobby? ChatGPT can be that assistant.

• Meal Prep: Imagine deciding what to cook, and ChatGPT suggests recipes based on what you have in your fridge, along with step-by-step instructions and nutritional advice.

• Pocket Coach: Need motivation or advice on staying fit? ChatGPT can suggest workouts, provide tips for mental wellness, and even help track your progress, acting as your personal coach.

• Craft Project Instructions: Looking to start a new craft project but don’t know where to begin? ChatGPT can offer ideas, detailed instructions, and even troubleshoot problems you might encounter along the way.

• Personal Learning: Whether it’s mastering a new language, understanding complex topics, or just satisfying your curiosity, ChatGPT can provide explanations, resources, and quizzes to make learning enjoyable and accessible.

• Learning new languages.

• Planning travel itineraries.

• Have fun!: Did you know that you can invert the roles and have ChatGPT asking you questions? try:
  
  Test my knowledge on ancient civilizations
  

• Can help you find a famous sentence or a word you cannot recall, just by giving some relevant context about what you are looking for.

💡 With ChatGPT, technology is not just for the tech-savvy. It’s a tool that everyone can use to make both work and daily life easier and more enjoyable. Think of it as having a smart, helpful friend, always ready to assist you in navigating both your professional and personal worlds.

Limitations

Chat GPT collects information, and when you ask something it returns what seems most likely. “Most likely” is not the only truth, just as the first google result is not the only answer, and not always the one you are looking for.

There are several risks associated with relying solely on summarized information from GPT or similar AI models, instead of evaluating different sources -i.e. through a search engine- Here are some key considerations:

1. Accuracy and Completeness: AI models like GPT summarize and generate responses based on patterns in data they were trained on. This can sometimes lead to inaccuracies or omitting critical nuances that might be captured through direct research from primary sources.

2. Bias and Perspective: AI models can inadvertently reflect biases present in their training data. Relying solely on AI for information could lead to a skewed understanding if these biases are not recognized and countered by checking diverse sources.

3. Recency of Information: Lack of real-time knowledge beyond its last update. GPT models are trained on a dataset that only includes information up to a certain point in time. They do not have access to or the ability to pull in the most current data or news, which can be critical depending on the topic.

4. Critical Thinking and Analysis: Evaluating different sources on a search engine encourages critical thinking as you compare perspectives, assess the credibility of each source, and synthesize information from various viewpoints. Relying on a single summarized source can limit this analytical process.

5. Depth of Understanding: While GPT can provide a broad overview, it may not be able to delve into the depth that specialized articles or studies offer. Detailed and technical topics might be oversimplified, missing crucial details.

6. Hallucinations. The content can be wrong altogether even when it may look correct. More on this later in the article.

7. Need for human oversight. This may be seen just as a consequence of all the above.

To mitigate these risks, it’s often beneficial to use GPT as a starting point for getting a general understanding or as a supplementary tool alongside traditional research methods. This approach allows you to leverage the strengths of AI in generating cohesive, understandable content while ensuring accuracy, depth, and breadth through direct source verification.

Hallucinations

In the context of Large Language Models (LLMs) like the one I am based on, hallucinations refer to instances where the model generates text that is factually incorrect or not grounded in reality. These hallucinations can occur due to various reasons, including biases in the training data, lack of context understanding, or errors in the model’s language generation process.

Here are a couple of known examples of hallucinations in LLMs:

1. Medical Misinformation: In the context of healthcare, LLMs have been observed to generate hallucinations that can potentially misinform patients or healthcare professionals. For example, a model might generate inaccurate medical advice or provide incorrect information about treatment options or symptoms of a particular condition.

2. False News: LLMs have also been known to generate false news articles or headlines, contributing to the spread of misinformation online. These hallucinated articles can resemble real news stories but contain fabricated information, leading to confusion and potential harm if believed and shared widely.

Addressing hallucinations in LLMs is crucial to ensure the reliability and trustworthiness of the generated output, especially in sensitive domains like healthcare or news reporting. Researchers are actively working on developing techniques to mitigate hallucinations and improve the overall quality and accuracy of LLM-generated text.

Search Engines vs Large Language Models

Chat GPT doesn't know anything that Google doesn't know, and it has a bunch of limitations and risks, so if you want information, it's better to use Google?

Choosing whether to use Google (or any other search engine) or ChatGPT depends largely on the nature of your query and what you’re looking to achieve with the information:

Use Google when:

1. Specific Facts or Research: You need precise, up-to-date information or statistics, like news, weather updates, or specific research studies.

2. Range of Sources: You want to see a wide range of opinions or sources on a topic.

3. Local Information: You’re looking for local businesses, services, or location-based data.

4. Complex Searches: You want to perform advanced searches using specific operators or filters to refine your search results.

Use ChatGPT when:

1. Conversational Understanding: You’re looking for a more interactive, conversational approach to explore topics or ideas, or you need explanations in simpler terms.

2. Synthesis of Ideas: You want a summary of a broad topic or need to combine information from various sources into a coherent narrative.

3. Creative Content: You need help with generating creative content, such as writing, brainstorming ideas, or artistic concepts.

4. Language Tasks: You need assistance with language-related tasks like translation, grammar correction, or writing improvement.

Why To Choose GPT Over Google

• Interactive and Iterative Learning: ChatGPT can help explain complex concepts in a step-by-step manner, making it useful for learning and understanding nuanced details.

• Personalization: It can tailor responses based on the conversation’s flow, providing a more personalized interaction.

• Language Processing: It’s particularly strong in generating human-like text, making it ideal for writing assistance, dialogue simulations, and more creative tasks.

Why To Choose Google Over GPT

• Accuracy and Timeliness: Google provides access to current information and can offer more reliable and precise data, which is crucial for research-based tasks.

• Comprehensive and Diverse Perspectives: It gives you access to a vast array of voices and perspectives, which can be important for forming a well-rounded view on controversial or debated topics.

In summary, the choice between Google and ChatGPT hinges on whether you value interaction and synthesis of information or if you need up-to-date, specific factual data sourced from a wide range of perspectives.

Introduction to prompt design and prompt engineering

These are 2 very interrelated concepts.

• Prompt Design refers to the process of creating clear, effective questions or statements that guide artificial intelligence or language models in generating useful, accurate responses. Think of it as asking someone in a clear way for exactly what you need.

• Prompt Engineering, is a more technical aspect. It involves optimizing these prompts to improve the quality, relevance, and accuracy of the AI’s responses. Think of it as using psycology to really understand how others think and obtain the curated response you are looking for.

Key Concepts

• Clarity: The prompt should be clear and specific. Ambiguity can lead the AI to misunderstand the request.

• Context: Providing the right amount of background information in a prompt helps the AI generate more accurate and relevant responses.

• Conciseness: While detail is important, being concise helps prevent overwhelming the AI with unnecessary information.

• Goal-Oriented: A good prompt has a clear objective. What do you want the AI to do? Generate text, answer a question, create an image?

Guidelines and Tips

1. Start Simple: Begin with straightforward prompts to see how the AI responds, then adjust as necessary.

2. Iterate and Refine: Don’t expect to get the perfect answer on the first try. It may take several iterations to refine your prompt to get the desired outcome.

3. Use Examples: When appropriate, provide examples in your prompt. This can guide the AI in understanding the format or type of response you’re looking for.

4. Be Specific: The more specific your prompt, the more likely you are to get a relevant response. However, balance this with the need to be concise.

5. Test Different Approaches: There’s often more than one way to design a prompt that achieves the same goal. Experimenting can reveal which is most effective.

6. Use Templates. Not only save time, also be more efficient if you get used to predefined structures that work for you for common tasks. As a general guideline you can use the frameworks below.

Prompt Frameworks

BAB

• Before

• After

• Bridge

Example

• We are nowhere to be seen on SEO rankings (BEFORE)

• Wish to be in the top 10 rankings on niche keywords (AFTER)

• Include the entire list of keywords targeted by our competitors (BRIDGE)

RTF

• Role

• Task

• Format

Example:

• You are a Facebook ad marketer (ROLE)

• Design a compelling Facebook ad campaign to promote a new line of fitness apparel for a sports brand (TASK)

• Create a storyboard outlining the sequence of ad creative, including story writing, visuals, and targeting strategy (FORMAT)

TAG

• Task

• Action

• Goal

Example:

• Elevate the performance of team members (TASK)

• Act as direct manager and assess the strengths and weaknesses of team members (ACTION)

• Improve the team performance so that the average user satisfaction score increases from 6 to 7.5 (GOAL)

CARE

• Context

• Action

• Result

• Example

Example:

• Launching a new line of sustainable clothing (CONTEXT)

• Help us create a targeted advertisement that emphasizes our environmental commitments (ACTION)

• With the desired outcome to increase awareness and sales (RESULT)

• A good example of a similar successful

Others

• Do-To:

  • Explain nostalgia to a kindergartener

  • Write a thank-you note to my interviewer

• Do-Context

  • Make up a story about Sharky, a tooth-brushing shark superhero

  • Plan a trip to experience Seoul like a local

How does it work?

It's not magic, it's science!

AI and ML are closer to statistics than to other disciplines. Probability is the key word.

There are three main stages: data collection and training, prompt interpretation, and generating responses.

1. Data Collection and Training

Collecting Training Data:

• ChatGPT is trained using a vast collection of text from the internet. This includes books, websites, articles, and other forms of written content. The goal is to expose ChatGPT to as much human language as possible, covering a wide range of topics, styles, and contexts.

Training Process:

• During training, ChatGPT learns to predict the next word in a sentence based on the words that come before it. It does this millions of times -processing up to 300 billion tokens-, adjusting its internal settings -up to 175 billion parameters- to get better at these predictions. This process is a bit like a highly advanced form of guesswork, where the model learns patterns, structures, and even the nuances of language.

Outcome:

• The result is a model that understands language to a significant degree and can generate coherent, contextually relevant text based on the great amount of patterns it has learned.

2. Prompt Interpretation

Receiving a Prompt:

• When you give ChatGPT a prompt, the model uses its training to understand what you’re asking. It analyzes the words and phrases you use to figure out the context and intent behind your question or statement. Whaever you are asking, it has already read before. But... exactly the same? Well, maybe not exactly, but ChatGPT pays a lot of attention... remember this word!

Context Understanding:

• The AI considers not just the immediate prompt but also the sequence of prompts and responses in a conversation. This allows it to maintain context and continuity, enabling more coherent and relevant interactions.

3. Generating Responses

Predicting Possible Responses:

• Based on the prompt’s analysis, ChatGPT generates a range of possible responses. It uses its understanding of language and context to predict what a suitable answer might look like, drawing on the patterns it learned during training.

Selecting the Best Response:

• From these possibilities, the model evaluates which response is most appropriate, coherent, and contextually relevant. This evaluation is based on its training and the objectives set during its development, such as being helpful, accurate, and safe.

Output:

• The selected response is then refined and presented as the final output. This process happens in seconds, making it seem like ChatGPT is responding in real-time.

How is it so different from previous ChatBots?

A bit of history.

The overview below shows how chatbots have evolved from simple, command-based systems to sophisticated tools capable of engaging in nuanced conversations:

Early Chatbots (1990s - Early 2000s):

• Based on predefined rules and logic.

• Users had to choose options or type exact keywords to get responses.

• Example: Automated customer service on websites.

Development of Natural Language Processing (Mid 2000s - Early 2010s):

• Chatbots began understanding variations in how people type and talk.

• Introduced “utterances” where users could input more natural language.

• Example: Early versions of Siri and simple conversational agents on messaging platforms.

Advancement with Machine Learning (2010s):

• Chatbots use large amounts of data to learn and predict responses.

• Begin to handle more complex conversations and tasks.

• Example: Google Assistant, which can set reminders and answer varied questions.

Introduction of Transformers and Attention Models (Late 2010s - Present):

• Newest type of technology in chatbots.

• Focuses on the context of entire conversations for better understanding and responses.

• Can maintain more coherent and extended interactions.

• Example: ChatGPT, which can generate human-like text and engage in detailed discussions.

More bellow about Transformers and Attention.

Attention is all you need!

Ashish Vaswani, affiliated with Google, is the lead author of the paper "Attention is All You Need", published in June 2017, which introduced the Transformer model and the mechanisms of attention, which has since become foundational in the field of natural language processing and beyond.

Transformers are a type of model used in machine learning, particularly for tasks that involve language, like translating between languages, summarizing text, or changing the tone of text. They are called “transformers” because they can transform one form of data into another. How do they work?

1. The transformer decomposes sentences into words

2. Attention mechanism: This is where it gets interesting. The transformer examines each word and decides which other words are important to understand its meaning. For example, in the sentence "The president announced a new policy", the word "president" might pay more attention to "announced" and "policy" because those words are key to understanding what the president did.

3. Understanding context: The attention mechanism allows the transformer to understand the context of each word, not just its individual meaning. This helps him or her to grasp more nuanced meanings that depend on other words around them.

I have created a different post about transformers:

And a bit more on it in

Legal and Ethical concerns

I am largely out of my expertise area here, so i wont mention much about this, but here is a line of explanation for some of the main legal and ethical concerns around the use of models such as ChatGPT:

Data Privacy

Ensuring user data is handled securely.

• It is critical to implement robust security and compliance measures to protect users' personal information and ensure that their interaction with the system is confidential and secure.

• The legal team together with the information security team is responsible for ensuring compliance with data protection laws such as GDPR in Europe or CCPA in California, implementing technical and organisational measures to protect user data.

• Misinformation: Mitigating the spread of inaccurate information.

  • The model should be designed and adjusted to minimise the generation of false or misleading content and promote fact-checking and accuracy in the answers provided.

  • Product development and AI research teams work on improving the accuracy of the model and its ability to handle truthful information. This includes fine-tuning algorithms and validating the information generated to minimise errors and misinformation

• Bias: Addressing inherent biases in the AI model.

  • It is crucial to conduct ongoing audits and train the model with diversified datasets to reduce inherent biases and ensure that responses are fair and equitable.

  • AI ethics, AI research and development teams are responsible for auditing and mitigating biases. This involves reviewing and improving training datasets and adjusting algorithms to reduce bias and ensure equitable responses.

• Intellectual Property: Understanding the ownership of AI-generated content.

  • Understanding and defining who owns the rights to AI-generated content is essential, in addition to respecting copyright laws when creating or using content based on or derived from copyrighted sources. Regarding intellectual property and the use of content generated by models such as ChatGPT (point 4), the situation may vary depending on the provider and specific policies.

  • Generally, OpenAI (the company behind ChatGPT) allows the use of model outputs for a wide range of applications, but with certain restrictions related to direct resale or misuse (such as the creation of misleading, defamatory or copyright-infringing information). Users are responsible for how they use the generated responses and must ensure that their use does not infringe copyright or trademark laws.

  • Compared to other competitors, most follow similar models where the generated output can be used freely at the user's own risk, but may differ in specific details about commercial re-use or the generation of derivative content. Some may require special licences for commercial use or impose additional restrictions on the type of content that can be generated or shared.

    It is important to review each provider's terms of service to fully understand the rights and responsibilities related to the use of AI-generated content.