SolGuruz Generative AI wiki
Generative AI Hub
Your Complete Guide to AI Technologies, Concepts and Trends
What each AI term means, how the ideas fit together, and the order to learn them in. Plain-language explainers for founders and CTOs planning an AI build, written by the engineers who ship them.
24 explainers6 topicsLast updated
01The basics
What is generative AI?
Generative AI is a category of artificial intelligence that creates new content, including text, images, code, audio and video, by learning statistical patterns from large datasets. It is powered by architectures like transformer models, large language models, diffusion models and generative adversarial networks, and businesses use it to automate content workflows, build AI agents and process documents at scale.
According to McKinsey's The state of AI in 2025, 88 percent of survey respondents report regular AI use in at least one business function, up from 78 percent a year earlier.
- A year earlier
- 78%
- 2025
- 88%
Source: McKinsey's The state of AI in 2025
02The big picture
How the ideas connect
The definition above, in one picture: the idea, the models behind it, how teams shape them, and what they build with them. Point at a term to see what it connects to.
1The idea
4Putting it to work
5At scale
03Learn it in order
The learning path
The map shows how the ideas fit together. To learn them, read these six stages in order: each one builds on the one before.
Stage 1 of 64 terms
Foundational Concepts
Start here if you're new to AI. These articles cover the building blocks that everything else is built on.
Written by Paresh Mayani
Stage 2 of 64 terms
Prompt Engineering and Interaction
Learn how to talk to AI models effectively in different ways, from basic prompting to advanced techniques.
Written by Lokesh Dudhat and Paresh Mayani
Stage 3 of 65 terms
Agentic AI and Automation
See how AI systems are growing from reactive chatbots to autonomous, goal-driven agents.
Written by Lokesh Dudhat and Paresh Mayani
Stage 4 of 63 terms
Core Techniques and Infrastructure
Check out which methods and infrastructure make generative AI work in real-time.
Written by Lokesh Dudhat and Paresh Mayani
Stage 5 of 63 terms
Generative Models and Architectures
Dive deep into the model architectures that do the content generation.
Written by Lokesh Dudhat and Paresh Mayani
Stage 6 of 63 terms
Data and Training
Understand how AI models are fed, trained, and improved with data.
Written by Paresh Mayani and Lokesh Dudhat
04Look it up
The dictionary
Already know the term you need? Every term is defined here in one line. Type to filter, or jump to a letter.
Showing 24 of 24 terms
A
- Agentic AI
AI that plans and acts toward an objective over multiple steps, rather than answering one prompt at a time.
Updated Aug 7, 2026
- AI Agents
A program that takes a goal, chooses a tool, acts, reads the result and decides the next step without being told each move.
Updated Aug 4, 2026
- AI Hallucinations
Confident model output that is not grounded in its source data, and the failure most likely to reach a user.
Updated Aug 6, 2026
- AI Orchestration
The layer deciding which model or agent runs next, holding shared state and handling retries when a step fails.
Updated Aug 7, 2026
C
- Context Engineering
Assembling the right information into a model's context window before it answers, rather than relying on the prompt alone.
Updated Aug 5, 2026
- Conversational AI
Systems that interpret intent across a dialogue and reply in natural language, holding state between turns.
Updated Aug 5, 2026
D
- Data Augmentation
Expanding a training set by turning existing samples into new variations, so a model generalizes better.
Updated Aug 6, 2026
- Diffusion Models
A model that generates an image by starting from noise and removing it step by step toward a target.
Updated Aug 6, 2026
F
- Fine Tuning LLMs
Continuing training on your own examples so a model adopts a task, format or domain it did not learn originally.
Updated Aug 6, 2026
- Foundation Models
A large model pre-trained once on broad data, then adapted to many downstream tasks instead of being trained per task.
Updated Aug 6, 2026
G
- Generative Adversarial Networks
Two networks trained against each other, one generating and one judging, until outputs are hard to tell from real.
Updated Aug 6, 2026
- Generative AI
AI that creates new text, images, code or audio by learning patterns from data, rather than only classifying what already exists.
Updated Aug 4, 2026
L
- Large Language Model
A model trained on vast amounts of text to predict the next token, which is how it produces fluent language.
Updated Aug 7, 2026
M
- Model Context Protocol
An open standard connecting AI models to tools, APIs and databases through one interface instead of custom integrations.
Updated Aug 7, 2026
- Multi-Agent Systems
Several specialized agents coordinating on one task, where the coordination and handoff design matters as much as the model itself.
Updated Aug 7, 2026
- Multimodal AI
A model that accepts and produces more than one kind of input, such as text alongside images, audio or video.
Updated Aug 6, 2026
N
- Neural Networks
Layers of weighted connections that turn an input into a prediction, and the architecture every modern AI model is built on.
Updated Aug 6, 2026
P
- Prompt Engineering
Designing the instructions given to a model so its output is specific, reliable and repeatable.
Updated Aug 4, 2026
R
- Retrieval Augmented Generation
Fetching relevant documents at query time and passing them to the model, so answers are grounded in your own data.
Updated Aug 5, 2026
S
- Synthetic Data
Artificially generated data used for training when real data is scarce, sensitive or unbalanced.
Updated Aug 5, 2026
T
- Transformer Architecture
This blog explains transformer architecture, the neural network design behind GPT, Claude, Gemini, and Llama. It covers how self-attention works, what each block in the diagram does, and the three main model types. It also explains the key limits and how to choose between using, fine-tuning, or building a model.
Updated Oct 26, 2026
V
- Vector Database
Storage built for embeddings, returning the nearest matches to a query rather than exact keyword hits.
Updated Aug 5, 2026
W
- What Is RLHF?
RLHF (Reinforcement Learning from Human Feedback) helps AI give answers people prefer. Learn its 3 stages, limitations, and how it compares to DPO.
Updated Oct 27, 2026
Z
- Zero-Shot, One-Shot and Few-Shot Prompting
Giving a model no examples, one, or a handful, to steer its answer without retraining it.
Updated Aug 4, 2026
05Common questions
Frequently asked questions about generative AI
Short answers to the questions people ask before they plan a generative AI build.
How does Generative AI work?
Generative AI uses neural networks like transformers and diffusion models, which detect patterns in large amounts of data. This helps in creating new content from a prompt provided by a user.
What are the different types of Generative AI?
The different types of Generative AI include
- Transformer-based models: Generative Pre-trained Transformers (GPT)
- Diffusion models: Stable Diffusion, DALL-E, Midjourney
- Generative Adversarial Networks (GANs)
Other types of Generative AI include Variational Autoencoders (VAEs), Autoregressive Models, and Flow-based Models.
Is ChatGPT the same as Generative AI?
ChatGPT and Generative AI are different. Generative AI is the broader category of AI that has the capability of creating new content. ChatGPT is only a specific chatbot developed by OpenAI that uses generative AI models like GPT-4.
What is the difference between generative AI and traditional AI?
Traditional AI mainly does analysis, classification, or prediction of outcomes from existing information. On the other hand, generative AI creates brand-new content. For example, a traditional AI system can classify an image, but a generative AI system can create a brand-new image from a text prompt.
What are the applications of generative AI?
The main uses of generative AI include content creation for marketing, software development, and customer support. Other uses of generative AI include faster drug development, 3D product development, and generating synthetic data to train other models in healthcare, finance, and entertainment.
What are the potential risks of generative AI?
The possible risks of generative AI are data privacy violations, algorithm bias, and security risks. Generative AI can also hallucinate content and cause copyright infringement. So you should always double-check content generated by Generative AI.
What are the ethical considerations of Generative AI?
The ethical considerations of Generative AI are called Responsible AI. Responsible AI means reducing bias, being transparent, and avoiding the creation of DeepFakes. Some of the key considerations of Responsible AI are protecting IP rights, the environment, and human accountability.
What is RAG in Generative AI?
Retrieval-Augmented Generation is an approach where an LLM is connected to an external data source. The data source is a vector database where factual information is stored. When a query is made to the Generative AI model, it retrieves real-time data from the vector database to give context to the LLM.
What is the difference between LLMs and Generative AI?
The difference between LLMs and Generative AI is that Generative AI is an umbrella term for any type of AI that creates content. LLM stands for Large Language Models. LLM is a subset of Generative AI but is only used for text generation, whereas Generative AI does text, image, video, and other forms of media generation too.
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