Generative Artificial Intelligence(Gen AI) – A
beginners’ guide
-Himanshu Kumar
Nov 2024
Introduction to Artificial
Intelligence
• Generative AI is a subset of Deep Learning
• Deep Learning is a subset of Machine
Learning
• Machine Learning is a subset of Artificial
Intelligence
•To gain a clear understanding of
Generative AI, basic knowledge of AI, ML,
and DL is essential
Artificial Intelligence (AI) —
From a Kid’s Perspective
•AI enables computers to think as humans do
•AI enables computers to understand, analyze data, and make decisions without constant human
guidance
AI Capabilities - Finding a Lost Dog
 To find your dog, the robot needs
human-like intelligence.
Following steps needs to be followed:
•Identify your dog
•Make a strategy to find your dog
•Act according to the situation
•Think and act like a human
Real-world Examples of AI
Applications
•Virtual Assistants (Siri, Alexa)
•Social Media Algorithms (Netflix,
Facebook)
•Online Shopping Recommendations
•Predictive Text and Autocorrect
•Healthcare Diagnostics
•Language Translation Services
•Fraud Detection in Banking
AI vs. Human Intelligence
Aspect
Learning Style
AI
Humans
Learns from data and examples. Crunches Learns by talking, experiencing, and thinking. Soaks up a variety of
numbers and patterns.
experiences and knowledge.
Thinking Speed Fast and efficient on trained tasks.
May take time but excels in complex thinking and creativity.
Memory Skills
Remembers facts and figures. Doesn’t
retain emotions and memories.
Retains events, emotions, and details. Memories are a mix of good and
bad experiences.
Feeling
Emotions
Does not feel emotions; sticks to rules and
Experiences a range of emotions, shaping reactions and identity.
patterns.
Flexibility
Factor
Smart but rigid; struggles in new
situations.
Creating Cool
Stuff
Creates within set limits, like an artist with Masters of creativity; limitless in generating new ideas, art, and
a specific canvas.
solutions.
Adapts to new situations and problem-solving; versatile.
Understanding Knows learned info but might miss
the Big Picture nuances and context.
Understands jokes, feelings, and cultural nuances; a comprehensive
understanding of situations.
Decision
Making
Capabilities
Uses a blend of logic, feelings, and what’s right to make decisions.
Makes decisions based on training and
rules.
Types of AI - Based on
Capability
Types of AI — Based on Capability
•Narrow AI (Weak AI)
•AI Systems designed & trained for a specific task or
narrow set of tasks
•Eg. Computer playing chess, conversational robots,
• email spam filters
•General AI (Strong AI)/General AI
•Can understand and learn any intellecutal task that a
human being can
•No such systems currently
•Super AI
•Represents a degree of intelligence where machines have the potential to exceed human
intelligence
•Still a hypothetical concept of AI
Types of AI - Based on
Functionality
1.Reactive Machines
•AI systems that operate based on present data
•No memory or past experiences
• Do not have memory to use past mistakes to inform future decisions
Example: Deep Blue (IBM’s chess-playing AI program)
2.Limited Memory
•Uses short-term memory to learn from the past and make better decisions for the
future
Examples: Self-driving cars, recommendation systems (Netflix, Amazon)
Introduction to Machine
Learning
Machine Learning is:
• A subset of Artificial Intelligence.
• Which enables machines (or computers) to learn from data and make
decisions.
• It’s already part of our daily lives, from recognizing songs to helping doctors
analyze medical images
• To put in simple words, it is analogous to teaching a robot (or any machine) by
giving lots of example pictures (or any other information)
Understanding Machine
Learning
Types of Machine Learning- Supervised
Learning
Some-real life examples:
• Email Spam Filtering:
Classifying emails as spam or
not spam.
• Image Classification:
Identifying objects or patterns
within images.
• Facial Recognition: Identifying
and verifying individuals based
on facial features.
• Financial Fraud Detection:
Identifying potentially fraudulent
transactions.
• Speech Recognition:
Converting spoken language
into text.
Types of Machine LearningUnsupervised Learning
Types of Machine LearningUnsupervised Learning
Some-real life examples:
•Clustering Customer
Segmentation: Segmenting
customers based on their
purchasing behavior.
•Anomaly Detection in
Cybersecurity: Identifying
unusual patterns or behaviors in
network traffic.
•Recommendation Systems:
Suggesting products, movies, or
content based on user behavior.
Types of Machine LearningReinforcement Learning
Reinforcement Learning is:
•Training a computer to make
decisions
•By rewarding good choices and
punishing bad ones
•Just as you might train a dog with
treats for learning tricks
Types of Machine LearningReinforcement Learning
Some-real life examples:
• AlphaGo: A computer
program that uses
reinforcement learning to play
Go at a superhuman level.
• Self-driving cars: Learning to
navigate traffic and respond to
various driving conditions.
• Algorithmic trading: Making
decisions on buying or selling
financial instruments based on
historical and real-time data.
Introduction to Deep Learning
Deep Learning is:
• A subset of Machine
Learning.
• Based on Artificial Neural
Network (ANN), which is
based on computational
models inspired by the
structure and functioning of
human brain
Biological vs Artificial Neural Network
•Biological Neural Network:
• Brain composed of billions of
interconnected neurons.
• Neurons interact,
communicate, and enable
humans to think and learn.
•Artificial Neural Network
(ANN):
• Computational network
inspired by the human brain.
• Consists of interconnected
nodes (neurons).
Architecture of Artificial Neural Network
• Primarily consists of three layers —
Input Layer, Output Layer and Hidden
Layers
• Input Layer: Receives data.
• Hidden Layers: Process information
and learn patterns.
• Output Layer: Provides the final
result or decision.
Working of Artificial Neural Network
Imagine a group of kids trying to recognize
a panda by sharing their observations.
• Each kid focuses on specific features
such as black-and-white fur, round face,
and distinct eyes.
• Individually, they might not fully
understand what a panda looks like,
• But by combining their insights, they
create a collective understanding.
Working of Artificial Neural Network
Scoring Approach:
• To refine their recognition skills, the kids keep track of their accuracy.
• If they correctly identify a panda, they gain points;
otherwise, they learn from their mistakes.
• Similarly, in neural networks, a scoring approach helps adjust the network’s parameters
to enhance accuracy over time.
To summarize:
• In artificial neural network, individual “neurons” specialize in recognizing specific
aspects.
• When combined, they contribute to recognizing the overall concept (panda).
• The network refines its understanding through repeated exposure, similar to kids
refining their panda recognition skills over time.
Deep Learning
•Definition:
• ANN with multiple
layers between input
and output.
•"Deep" Meaning:
• Capable of learning
complex patterns.
Important points about Deep Learning
Subset of ML:
• Deep learning is a subset of machine learning, which is a subset of AI.
Inspiration:
• Based on how our brains work.
Artificial Neural Network (ANN):
• Mimics biological neural networks.
Learning from Data:
• Learns by processing many examples and adjusting connections.
Handling Complex Problems
• particularly effective for solving complex problems where traditional
approaches may struggle.
Machine Learning vs Deep Learning
Introduction to Generative AI
Generative AI is:
• A type of artificial
intelligence
• that can create new things,
for example artwork, music,
or even realistic images.
• without being explicitly told
what to create
Traditional vs Generative AI
• Traditional AI: Focuses on
specific tasks or problemsolving
• Generative AI: Exhibits
creativity similar to human
creativity, creates new
content, ideas, or solutions
Example of Generative AI
•What if a computer program could
create new things all by itself!
•What if the computer program was
tasked with creating a completely new
animal, say a “lion-cow-butterfly”
combination
This is Generative AI — A machine (or
computer) which has imagination and
creativity to draw pictures, tell stories,
or even make up new games without
anyone showing it how.
Evolution of Gen AI and where Gen AI fits
in AI Hierarchy
Discriminative vs Generative AI
• Discriminative AI focuses on
learning class boundaries and
is used mainly for classification.
• Generative AI models the joint
probability distribution and is
used for both classification and
data generation tasks.
Discriminative vs Generative AI
Discriminative AI
• Focuses on learning the
boundary between classes
• Predicts the probability of a
label given the input data
• Doesn’t model the distribution
of the data
• Examples: Logistic Regression,
SVM, Neural Networks for
Classification
Generative AI
• Focuses on modeling the joint probability
distribution of the data
• Can generate new data that is similar to
the training data
• Can be used for both classification and
generation tasks
• Examples: Naive Bayes Classifier,
Gaussian Mixture Models, VAEs, GANs
Generative models
The generative models:
• learns the underlying set of
data and generates new data
the closely mimics the original
data
• are mainly used to create new
content, such as images, text,
or even music which looks
exactly the same as what might
be created by humans
• Uses unsupervised learning
approach
Most common generative models are:
•
•
•
•
Variational Autoencoders (VAEs),
Generative Adversarial Networks (GANs)
Limited Boltzmann Machines (RBMs)
Transformer-based Language Models
Real-life applications of Gen AI
Text Generation: ChatGPT, Virtual
Assistants, Chatbots
Image Generation: DALL-E, Image
editing, Face generation
Video Generation: Entertainment,
Advertisements, Training simulations
Voice Generation: Natural sounding
virtual assistants, Audiobooks
Healthcare, Drug Discovery, Gaming,
Art Generation, Software
Development, Finance
Popular Generative AI tools
•ChatGPT: Conversational AI by OpenAI
•GPT: Engine behind ChatGPT by
OpenAI
•AlphaCode: AI-powered coding engine
by DeepMind
•GitHub Copilot: AI-powered code
completion tool by GitHub and OpenAI
•Bard: Conversational Generative AI
chatbot by Google
•Microsoft Copilot: AI-powered assistant
by Microsoft
•DALL-E: Image generation by OpenAI
•StyleGAN: High-quality synthetic image
generation by NVIDIA
Generative AI stack
Foundation Layer, L1
•Consists of specialized hardware
like GPUs
•Focuses on efficient computation
and parallel processing
•NVIDIA leads in this layer
Model Layer, L2
•Includes advanced models like
Large Language Models (LLMs) and
Visual models
•Enables synthesis of novel data
instances
•OpenAI is the leader in this layer
Generative AI stack (contd.)
Tooling Layer, L3
•Comprises essential tools and services like ETL processes, vector search,
RLHF, and model optimization
•Streamlines and optimizes the deployment and utilization of generative AI
models
•Investments include Segmind; other companies like Portkey and Branch.ai are
notable
Application Layer, L4
•Encompasses apps developed using APIs from the Model Layer and services
from the AI Tooling Layer
•Represents the tangible manifestation of generative AI technology
•Examples: Notion’s writing assistant, Jasper.ai
Introduction to Large Language Models
A generative AI system which
needs to generate human-like text
needs Large language models.
Language model is:
• a type of machine learning
model
• which uses various statistical
and probabilistic techniques
• to predict probability of a given
sequence of words in a
sentence or phrase.
Understanding Large Language Models
In simple words,
• language model is designed to
predict next most suitable word
to fill in a blank space in a
sentence or phrase, based on
the context of the given
sentence/phrase.
• The most unique and powerful
point about large language
models is their ability to
generate human-like text,
summarize, and predict
content based on vast
amounts of data.
Understanding Large Language Models
(contd.)
• Large Language Models
(LLMs) play a crucial role in
Generative AI by generating
human-like text
• LLMs are trained on vast
text datasets and use
advanced neural network
architectures
• Applications of LLMs range
from virtual assistants and
chatbots to translation, text
generation, summarization,
and sentiment analysis
Natural Language Processing
Natural Language Processing (NLP) is a
subset of AI, which focuses on the
interaction between computers and
humans through natural language
•refers to the process of enabling
computers to understand human
language and communicate with us in the
same language.
•uses algorithms to analyze, understand,
and generate human language.
•helps computers understand the context,
and sentiment behind words and
sentences.
Relation between Natural Language
Processing (NLP) and Large Language
Model (LLM)
Aspect
Natural Language Processing (NLP)
Subset of AI focusing on human-computer
interaction through natural language
Large Language Models (LLM)
Any model designed for NLP tasks, with a
focus on generating human-like text
Main Focus
Processing and understanding human
language
Generating and predicting human-like text
based on context
Techniques
Used
Wide range: Rule-based methods, machine
learning, deep learning approaches
Primarily deep learning techniques
Definition
Predictive
Predicts next word or sentence based on
Capabilities context and learned patterns
Predicts probability of next word in the
sequence
Architectural
Various architectures
Basis
Primarily based on transformer-based
models and artificial neural network
Applications
Text classification, sentiment analysis,
language translation, chatbots
Text generation, summarization, translation,
sentiment analysis, chatbots
Focus Area
Broad spectrum of language processing tasks
Specific focus on generating human-like
text and understanding context
Large Language Models and Generative AI
• Large Language Model
(LLM) are a subset of
Generative AI.
• While generative AI can
generate many types of
content such as text, image,
video, code, music etc., LLM
is focused on generating text
only.
Real-world Applications of LLMs
•Virtual Assistants: Siri, Alexa, Google
Assistant
•Chatbots: ChatGPT
•Language Translation: Google
Translate
•Text Generation: Story writing,
product descriptions, emails
•Summarization: Document and article
summarization
•Sentiment Analysis: Analyzing
sentiments in text data
•Content Recommendations: Netflix,
YouTube, Amazon
Some popular LLM Examples
•GPT (Generative Pre-trained
Transformers): GPT-1 to GPT-4
•BERT (Bidirectional Encoder
Representations from Transformers)
•LaMDA (Language Model for
Dialogue Applications): Now known as
Gemini
•LLaMA (Large Language Model
Meta AI) by Meta AI
Challenges in building LLM powered
applications
Deploying & Managing Applications
- Complexity in cloud deployment
- Challenges in managing code,
prompts, and configuration
- Difficulty in tracking and managing
model parameters
Navigating Cloud Resources
- Technical challenges in running
hosted model APIs
- Cost optimization and GPU
management
- Adoption of complex techniques for
cost reduction
Challenges in building LLM powered
applications(contd.)
Tracking Application Performance
- Addressing hallucinations and nonsensical outputs
- Balancing prompt context to avoid incorrect answers
- Tying model outputs to business metrics for
accurate assessment
Access Control & Credential Management
- Managing credentials and access controls for data
systems
- Ensuring data privacy and avoiding information
leakage
Budgeting and Costs
- High costs associated with model APIs and cloud
GPUs
- Importance of managing expenses and setting
budgets
Other challenges with using LLMs & how to
best address them
Challenges
Concerns
Solutions
-
Bias and
Fairness
Potential biases in outputs due to training on
biased datasets.
Privacy and
Data
Protection
Access to extensive text data raises privacy
issues.
Accountabilit
Ethical use and transparency in deploying
y and
models.
Responsibility
-
Careful selection and processing of training
data.
Ongoing monitoring and fine-tuning.
Implement measures to detect and rectify
biased outputs.
-
Robust data security measures.
Anonymizing and encrypting data.
Controlling access and regular auditing.
-
Establish clear guidelines and principles.
Prioritize transparency with users and
stakeholders.
Implement accountability mechanisms for
ethical breaches.
-
Misinformati
on and Dual Potential misuse and spread of false information.
Use
Collaborative efforts among developers,
policymakers, and regulators.
Promote innovation while safeguarding
against misinformation.
Introduction to Prompt Engineering
Prompt engineering:
• includes designing and
optimizing prompts
• in a strategic manner
• to generate more accurate and
desired response from AI
systems.
Instead of asking a general
question, prompt engineering
involves providing specific
instructions or context to get
better results.
What is a Prompt?
•Prompts are the inputs or
questions given to AI systems to
get specific responses.
• Example: "Tell me a story which
includes animal characters. The
story is targeted for kids."
Key Point: The specificity and
detail of the prompt guide the AI
to generate the desired content.
The importance of asking the right questions
to AI systems for accurate responses.
Techniques in Prompt Engineering
1. Specificity
Example:
Non-specific: “Tell me about cars.”
Specific: “Can you describe the features
of electric cars compared to traditional
gasoline cars?”
2. Contextualization
Example:
Non-contextualized: “Write a review of
this product.”
Contextualized: “Write a review of this
product focusing on its performance for
outdoor activities.”
3. Fine-tuning
Process: Adjust the prompt iteratively
based on AI system's output.
Examples of good and bad prompts
Bad Prompt vs. Good Prompt
•Bad Prompt: “Write a short story”
• Good Prompt: “Write a short story
about a detective solving a
mysterious murder case.”
•Bad Prompt: “Explain
photosynthesis”
• Good Prompt: “Explain the
process of photosynthesis in
plants, including the role of
chlorophyll and sunlight.”
•Bad Prompt: “What should I do
today?”
• Good Prompt: “Suggest some fun
outdoor activities for a sunny day.”
How to write effective prompts?
1.Be Clear and Specific
2.Provide Context
3.Use Examples
4.Ask Specific Questions
5.Include Constraints
6.Test and Iterate
7.Focus on Clarity Over
Creativity
Credits
8 part article written by Raja Gupta
• https://medium.com/@raja.gupta20/generative-ai-for-beginners-part-1introduction-to-ai-eadb5a71f07d
Other articles
•https://medium.com/all-in-capital/how-to-think-about-generative-ai-7f8f7b10bec7
•https://blog.gopenai.com/large-language-models-llms-a-brief-historyapplications-challenges-c2fab10fa2e7
•https://generatingconversation.substack.com/p/the-easiest-part-of-llmapplications
Kindly note: All images have credits within the images, wherever applicable
Further suggested reads
Kushal Bhagia
•https://medium.com/all-in-capital/how-to-think-about-generative-ai-7f8f7b10bec7
Ambika
•https://blog.gopenai.com/large-language-models-llms-a-brief-historyapplications-challenges-c2fab10fa2e7