Assisted writing
Emails, product sheets, posts — with brand voice in the prompt.
What is Generative AI? Definition and explanations
Generative AI creates new content — text, code, images, audio — from a prompt and a model trained on huge corpora. It does not “understand” like a human: it predicts the most likely next tokens.
In one sentence
Generative AI produces new content from a prompt and a model.
Key points
- Large language models (LLMs) are the most visible form, not the only one.
- Quality depends on prompts, context (RAG), and model limits.
- Hallucinations and sensitive data leakage are operational risks highlighted by NIST.
- Training data provenance and licences affect commercial use.
- It augments — not replaces — domain expertise and human review.
Term at a glance
- Generative AI
- GenAI · Generative artificial intelligence
- French term
- IA générative
- Domain
- Artificial intelligence
- Category
- Generative AI
- Level
- Intermediate
Defining generative AI beyond chatbots
It speeds drafting, prototyping and assistance, but can hallucinate: invent plausible but false facts.
Enterprise value appears when you frame it: approved sources, human validation, data policies.
RAG and fine-tuning are two ways to ground it in your reality instead of the generic web.
How to deploy generative AI safely
- 01
Map high-value cases
Start where mistakes are reversible: drafts, variants, internal summaries — before regulated or financial decisions.
- 02
Ground the model on your sources
RAG, system instructions, and approved knowledge bases cut fabrications about your catalogue or contracts.
- 03
Control access and data
Ban unnecessary personal data in prompts, pick compliant processing regions, and use zero-retention options when available.
- 04
Measure and train staff
Track review rates, corrections, time saved; train teams on limits and citing sources.
A concrete generative AI example
A writer gets a first SEO draft from a brief; they correct facts and tone before publishing.
Business uses of generative AI
Faster development
Code sketches, tests, inline docs under peer review.
Tier-1 support
Suggested answers from the knowledge base; human escalation on low confidence.
Visual creation
Mock-ups and ad variants with legal review on copyright.
Benefits and limits of generative AI
- Cuts time to an acceptable first draft
- Explores creative or linguistic variants quickly
- Integrates via API without full rewrites
- Can specialise with RAG or targeted fine-tuning
- May state false facts confidently
- Variable cost by tokens, images, and volume
- Vendor dependency and model churn
- IP questions on inputs and outputs
Business impact of generative AI
Treated as a draft accelerator — not a source of truth — generative AI frees senior time for judgement and client relationships. Align privacy law, acceptable-use policy, and cloud vs local choices from the pilot to avoid shadow IT uploading client files to consumer tools.
Frequently asked questions
Is generative AI always ChatGPT?
No. ChatGPT is one interface over proprietary or open-weight LLMs. Other models cover image, audio, or code; guarantees and costs differ.
Can we avoid sending data to the cloud?
Yes, with open-weight models on-prem or in a private VPC, at the cost of GPU capacity and ops. Document the cost, quality, and update trade-off.
How do you catch hallucinations?
Require citations to RAG documents, cross-check numbers internally, and keep human review on external or contractual content.
Related terms
Sources and references
Want a GenAI pilot grounded on your documents — without shadow IT?
Plan your GenAI pilot