What is AI (and what is Generative AI)? Complete guide

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Introduction

The phrase “what is generative AI” has dominated searches, technical conversations, and business strategies in recent years. In this article we explain, clearly and with the right amount of technical detail, what artificial intelligence (AI) is, how generative AI differs from other approaches, the role of Large Language Models (LLMs), the phases of AI, and the practical tools used to build business solutions (for example: n8n, LangFlow, LangGraph, LangChain and others).

The text is intended for decision-makers, developers and product professionals who want to understand both why and how to deploy AI in the company.

What is AI? (short definition)

Artificial Intelligence (AI) is the field of computer science that develops systems capable of performing tasks that would normally require human intelligence, such as recognizing patterns, making decisions, translating languages and generating content. AI is not a single algorithm: it’s an umbrella that includes techniques (statistical, symbolic, optimization), models and data pipelines.

AI vs Machine Learning vs Deep Learning: what’s the difference?

• AI (Artificial Intelligence): a broad term that includes any system capable of intelligent behavior.

• Machine Learning (ML): a subfield of AI that creates models from data instead of explicit rules programmed by humans. Examples: regression, trees, SVMs.

• Deep Learning (DL): a subfield of ML that uses deep neural networks (multiple layers) and has driven many recent advances in computer vision, speech recognition and natural language processing (NLP). Standard reference on fundamentals: Deep Learning (Goodfellow, Bengio, Courville).

Brief history (timeline with milestones)

1. 1950s (Foundation): Alan Turing and his question “can machines think?” and early work on symbolic automation and reasoning (Turing, McCarthy).

2. 1960s–1980s (Symbolic AI): rule-based systems, logic and knowledge (expert systems).

3. 1990s (Statistical learning): probabilistic and statistical methods gain traction (HMMs, SVMs).

4. 2010 onward (Deep Learning and Big Data): deep neural networks, GPUs and large datasets led to advances in vision, speech and language.

5. 2017 (Transformer): the paper Attention Is All You Need introduced the transformer, an architecture that revolutionized NLP and made efficient training of LLMs possible.

6. 2020 (GPT-3): scalable autoregressive models showed that increasing parameters and data produces emergent capabilities.

What is Generative AI? (definition and scope)

Generative AI is the set of AI techniques capable of creating new content (text, image, audio, video, code) that did not exist before, based on patterns learned from data. Instead of just classifying or predicting, generative models produce plausible samples from the distribution of the training data. Companies and researchers define generative AI as models that can synthesize information based on prompts or context.

Examples of generative AI: GPT (text), DALL·E / Imagen (images), VQ-VAE / diffusion models (image synthesis), audio models that generate voice and music.

Large Language Models (LLMs): basic mechanics

LLMs are language models trained on enormous collections of text to predict the next word (or token). Although different architectures exist, most modern LLMs use variants of the transformer architecture (attention). Key concepts:

• Pre-training: training on a large text corpus to learn the statistics of language (representations).

• Fine-tuning / Instruction tuning / RLHF: fine adjustments for specific tasks or to follow instructions (e.g.: RLHF, Reinforcement Learning from Human Feedback).

• Embeddings: convert text into numeric vectors for semantic search (used in RAG, Retrieval-Augmented Generation).

Foundational papers: Attention Is All You Need (Vaswani et al., 2017), Language Models are Few-Shot Learners (GPT-3, Brown et al., 2020) and works on BERT (Devlin et al., 2018).

How generative AI works in practical terms

1. Data: web, internal databases, logs, documents; quality and cleaning matter.

2. Training: tuning millions/billions of parameters on GPUs/TPUs; requires infrastructure or managed services (cloud).

3. Inference: generate a response from prompt + context; may involve RAG to use up-to-date knowledge.

4. Post-processing and safety: filters, factuality checks, moderation.

For many enterprise applications, the practical path is to use LLMs and combine them with pipelines (e.g.: RAG, rule-based validation, orchestration workflows). OpenAI, Google and other research organizations describe these steps in posts and technical whitepapers.

Phases of AI and AI agents that perform actions

Historically we talked about reactive AI (responds to inputs). Today agents are emerging that not only react but plan, interact with tools and execute actions in digital environments.

• Reactive systems: classifiers, FAQ assistants.

• Deterministic pipelines: traditional RPA that follows rules (e.g.: automations without language heuristics).

• LLM-based agents: use models to decide sequences of steps (call APIs, run scripts, fetch data, interact with users) and even plan strategies. Tools like LangChain and LangFlow help build these agents. Important concepts include planning, execution, the observe-act loop and long-term memory.

  
  

Practical example: an agent can receive a chat request, retrieve relevant documents via RAG, execute a macro in the ERP via n8n to collect data and then deliver a response or open a ticket, all coordinated by a control flow decided by the LLM.

Tools and ecosystem to develop solutions (enterprise focus)

n8n: automation and orchestration

n8n is a fair-code automation platform that allows building workflows connecting APIs, databases and legacy systems. It’s useful to integrate LLMs into business processes, orchestrate model calls and run actions in internal systems.

LangFlow / LangGraph / LangChain: building LLM agents and flows

• LangFlow: visual interface to build pipelines that combine LLMs, prompts, vectors and tools; facilitates experimentation without building the backend from scratch.

• LangChain / LangGraph: frameworks that implement patterns to build LLM-first applications, including agents, RAG and memory/state tools. These projects define libraries and design patterns widely adopted for production agents.

Vector databases and semantic storage

Vectors (embeddings) are the basis for semantic search and RAG. Solutions like Pinecone, Weaviate, Milvus and others are commonly used in production architectures.

Infrastructure and managed services

Companies can choose between training models in-house (expensive and complex) or using hosted models (OpenAI, Anthropic, Cohere, Google, etc.) and combine them with orchestration infrastructure (Kubernetes, serverless) and observability.

Proven enterprise applications (cases and examples)

1. Customer service: advanced chatbots that triage, generate responses and escalate complex cases. (RAG improves accuracy).

2. Content generation: drafts of text, emails, reports and marketing reduces creation time.

3. Process automation (RPA + LLM): data extraction, form filling, reconciliation.

4. Analysis and summarization: turn long contracts into action points and risk highlights.

5. Developer assistance: code generation, PR review, automated testing.

Studies and reports from companies like OpenAI, IBM and academic publications show productivity gains and cost reductions in high-volume text and decision processes.

Essential technical considerations before starting

• Data and privacy: ensure compliance with privacy laws, anonymization and access controls.

• Data quality: models reflect biases in the data. Curation improves performance and reduces risks.

• Cost and latency: inference on large LLMs has cost; using smaller or hybrid models (on-prem + cloud) may be necessary.

• Factuality and verification: LLMs can “hallucinate” answers. Strategies: RAG, rule validation, human checks.

• Monitoring and observability: log prompts, responses, costs and satisfaction metrics.

Risks, ethics and governance

Risks with generative AI include: biases, misinformation, privacy, security (e.g.: generation of malicious code), and legal challenges about copyrights for generated content. Governance should include usage policies, review teams and technical mechanisms (filters, sensitive content detection). Sources such as FT reports and research company guidelines describe practical approaches.

How to start: practical roadmap (5 steps)

1. Identify high-value cases: where volume + repetition + financial impact are large (e.g.: support, contracts).

2. Proof of Concept (PoC): build a flow with RAG + LLM and clear metrics (accuracy, time, cost).

3. Integration with systems: use n8n, LangFlow or SDKs to connect the model to internal systems.

4. Governance and privacy: set rules, logs and human review processes.

5. Scaling and observability: optimize prompts, caches, and consider fine-tuning or instruction tuning when needed.

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