No Bad Questions About AI
Definition of Generative AI
What is generative AI?
Generative AI, often called gen AI, is a type of artificial intelligence that creates new, original content based on the data it receives. It can generate text, images, audio, video, animations, 3D models, and more. These models can produce entirely original outputs from simple inputs, with results varying based on their training.
Generative AI vs AI: what is the difference?
AI is a broad field of computer science focused on building systems that can analyze data, recognize patterns, and make decisions with minimal human intervention. AI models typically classify, predict, or optimize outcomes based on existing information.
Gen AI is a specialized subset of AI designed to create new content. Instead of only analyzing or predicting, generative models learn from large datasets and use that knowledge to produce original outputs such as text, images, or code.
Let's elaborate on key differences:
1. Purpose
- AI: Understands existing data to make decisions, predictions, or classifications.
- Generative AI: Produces new content or data based on learned patterns.
2. Output
- AI: Labels, recommendations, scores, predictions.
- Generative AI: New content – text, images, audio, video, designs, or synthetic data.
3. Data requirements
- AI: Often effective with smaller, task-specific datasets.
- Generative AI: Requires large, diverse datasets to generate high-quality outputs.
4. Transparency
- AI: Can be more interpretable, depending on the model.
- Generative AI: Often functions as a "black box," making reasoning less transparent.
5. Use cases
- AI: Fraud detection, recommendation systems, route optimization, anomaly detection, predictive analytics, and speech recognition.
- Generative AI: Content creation, design, prototyping, code generation, scientific simulation, and hypothesis modeling.
How does generative AI work?
Generative AI typically operates in three main phases: training, tuning, and ongoing generation with evaluation. Together, these phases allow models to learn from massive datasets, adapt to specialized tasks, and continuously improve their outputs.
1. Training: building the foundation model
The process starts with creating a foundation model—a large neural network trained on huge volumes of raw, unstructured data such as text, code, and system logs.
During training, the model performs millions of self-supervised tasks (like predicting the next word or reconstructing part of an image) and adjusts its parameters to reduce errors. This builds a detailed internal representation of patterns and relationships across the data.
2. Tuning: adapting the model to specific tasks
A foundation model is powerful but broad. It understands many types of content, but may not generate specialized output with high accuracy. Tuning narrows the model's capabilities for a specific application, such as customer support, medical summarization, or legal drafting. There are two main ways to tune a model:
▫️FINE-TUNING
Fine-tuning involves training the model on labeled datasets tailored to a particular use case. Example: A team building a customer service chatbot might supply thousands of question–answer pairs from real user interactions.
This step is often labor-intensive and may require outsourced data-labeling teams.
▫️REINFORCEMENT LEARNING WITH HUMAN FEEDBACK (RLHF)
In RLHF, human evaluators score or correct model outputs. The model learns from this feedback to produce more accurate, helpful, or safe responses.
RLHF can involve: comparing multiple responses to the same prompt, ranking outputs, and directly correcting chatbot answers during interactions
Together, fine-tuning and RLHF transform a general model into a specialized one.
3. Generation, evaluation, and continuous improvement
Once deployed, generative AI applications produce content based on prompts or inputs. Developers and users then evaluate the outputs, identify errors, and apply additional tuning to improve factual accuracy, tone, or task-specific performance. Here is where RAG fits in.
▫️ RETRIEVAL-AUGMENTED GENERATION (RAG)
Even after tuning, a model still has a limitation: It only "knows" what was in its original training data. So if you need up-to-date or domain-specific information, retraining or fine-tuning is expensive and slow.
RAG solves this by letting the model pull information from external sources at generation time, for example: company documentation, policies, databases, and real-time data sources
RAG doesn't replace tuning. It adds a live knowledge layer on top of it, helping the model generate answers that are both accurate and current, without needing to retrain the entire foundation model.
Generative AI works by training huge models on large datasets, tuning them for specific tasks, and continually improving their outputs through evaluation and feedback. This process enables models to produce new, high-quality content across text, images, audio, video, and beyond.
What is the main goal of generative AI?
The main goal of generative AI is to create new, meaningful content by learning from existing data. It focuses on:
▫️ Creativity: Producing original ideas, text, images, or designs.
▫️ Efficiency: Automating content creation to save time and resources.
▫️ Innovation: Enabling new solutions in areas like research, product design, and personalized experiences.
These capabilities translate into practical applications across creative work, productivity tools, and enterprise systems.
How to use generative AI?
Generative AI can be applied across multiple domains. Its uses generally fall into three broad categories:
1. Creative & content-based applications
- Generating text, images, video, design assets, or code
- Creating marketing materials, product mockups, and creative concepts
- Assisting with ideation, brainstorming, and prototyping
2. Productivity & workflow automation
- Summarizing documents, emails, and reports
- Drafting text, preparing presentations, and automating routine communication
- Extracting information from large datasets and generating structured outputs
3. Technical & enterprise applications
Generative AI is increasingly used in more specialized, high-stakes domains:
▫️ SOFTWARE & IT OPERATIONS
- Generating code, tests, documentation, and infrastructure configs
- Supporting DevOps workflows with automated insights
- Explaining system failures or logs in human-readable language
▫️ DATA & RESEARCH
- Generating hypotheses, simulating scenarios, and synthesizing datasets
- Assisting with scientific or engineering research
▫️ CYBERSECURITY
- Detecting anomalies in logs and network traffic
- Summarizing vulnerabilities and generating remediation steps
- Assisting analysts with incident response workflows
- Simulating cyberattacks to test defenses
- Producing synthetic data for secure model training
📖 For more detailed examples and enterprise-centric use cases, see our in-depth article Generative AI for Enterprises.
Key Takeaways
- Generative AI is a type of artificial intelligence that creates new content by learning patterns from large datasets.
- It differs from traditional AI because it doesn't just analyze or predict; it generates original outputs.
- Generative AI works by training a large foundation model, tuning it for specific tasks, and continually improving its outputs through evaluation and techniques such as RAG, which incorporates up-to-date external information.
- Its main goal is to create meaningful content that enhances creativity, boosts efficiency, and drives innovation.
- You can use generative AI for creative work, productivity tasks, technical applications, and enterprise needs. It also plays an important role in cybersecurity, helping teams detect anomalies, analyze vulnerabilities, support incident response, simulate attacks, and safely train models with synthetic data.
