Knowledge assistants
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Technology / Generative and Language
Generative AI
Generative AI produces new content: text, images, code, audio and structured data. It has made AI accessible to almost every role in an organisation. Using it well means pairing its fluency with grounding, evaluation and human review.
What is Generative AI?
Short answer
Generative AI is a category of artificial intelligence that creates new content, such as text, images, code, audio or video, by learning patterns from large amounts of training data and producing outputs that follow those patterns in response to prompts or other inputs.
Generative models are typically trained on very large datasets to predict what comes next, whether that is the next word in a sentence or the structure of an image. This training produces models that can respond to instructions in natural language and adapt to many tasks without task-specific training.
In organisations, the most valuable uses combine generative models with company knowledge and workflows: assistants that answer from approved documents, copilots that draft within business applications and pipelines that transform unstructured information into structured data. Grounding, access control and evaluation turn a general model into a dependable business tool.
Related concepts
- Large Language Models
- RAG
- Prompting
- Agentic AI
- Multimodal AI
- Fine-tuning
- Guardrails
How it works
How does Generative AI work?
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Pre-training
A model learns general patterns of language or images from very large datasets.
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Instruction tuning and alignment
The model is further trained to follow instructions and behave more helpfully and safely.
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Prompting and context
Users or applications provide instructions, examples and relevant information such as retrieved documents.
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Generation
The model produces output step by step, choosing likely next tokens or refining an image.
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Validation and review
Applications check format, grounding and policy, and people review where needed.
Applications
Where is Generative AI useful?
Common applications across industries. Suitability always depends on the specific data, process and constraints.
Drafting and summarising
Producing first drafts of reports, emails and documents, and condensing long material.
Software development
Code suggestions, test generation, documentation and code explanation.
Data extraction
Turning unstructured text and documents into structured fields.
Translation and localisation
Adapting content across languages with human quality review.
Creative and marketing support
Generating variations of copy or images within brand and legal review.
Limitations
Limitations and when not to use it
Every technology has boundaries. Knowing them early avoids expensive disappointment.
- Models can produce plausible but incorrect or fabricated information.
- Outputs vary between runs, which complicates testing.
- Knowledge in the model may be outdated without retrieval.
- Sensitive data may be exposed without proper controls.
- Copyright, attribution and content ownership questions need policy decisions.
Common misconceptions
Generative AI knows facts reliably.
Models generate likely text, not verified facts. Grounding and checking are needed for accuracy.
Fine-tuning is required for company use.
Most business applications work best with retrieval and good prompting; fine-tuning suits specialised needs.
Generative AI is only for chatbots.
Many valuable uses are inside workflows, such as extraction, classification and drafting steps.
Comparison
Generative AI compared with predictive AI
Both are forms of machine learning, but they solve different kinds of problems.
| Aspect | Predictive AI | Generative AI |
|---|---|---|
| Main output | A label, score or number | New content such as text, code or images |
| Typical tasks | Forecasting, classification, risk scoring | Drafting, summarising, answering, transforming |
| Training data needs | Task-specific labelled data | Pre-trained on broad data; adapted with prompts or retrieval |
| Evaluation | Standard metrics such as accuracy or error | Task-specific criteria, often including human judgement |
| Main risks | Bias, drift, poor calibration | Fabrication, data leakage, prompt injection, inconsistency |
Decision guide
How to evaluate Generative AI for your organisation
Questions worth answering before investing. They form the basis of our feasibility and proof-of-concept work.
| Question to ask | Why it matters |
|---|---|
| Which tasks will generative AI perform, and who reviews output? | Clear task boundaries and review points control risk. |
| What knowledge must answers be grounded in? | Source quality and access permissions determine answer quality. |
| How will we measure quality? | An evaluation set built with subject experts is essential. |
| What data can be sent to which models? | Data classification and model hosting choices protect sensitive information. |
| What will it cost at expected usage? | Usage-based charges from model providers can grow quickly without controls. |
AARHIT services
Services that apply Generative AI
How we put this technology to work for organisations.
Hybrid Intelligence
Generative AI and Large Language Models
Build grounded generative AI applications using large language models, retrieval, evaluation and guardrails.
Explore service Hybrid IntelligenceConversational AI and Virtual Assistants
Create assistants for customers or staff that answer from approved knowledge and hand over to people when needed.
Explore service AutomationAI Workflow Automation
Embed language models, classifiers and retrieval into business workflows with validation and review steps.
Explore service ResearchAI Model Evaluation and Optimization
Measure model quality, bias, robustness, latency and cost, then tune models and prompts against clear criteria.
Explore serviceRelated technologies
Industries
FAQ
Generative AI FAQs
What is generative AI used for in business?
Common uses include knowledge assistants, drafting and summarising documents, customer support copilots, code assistance, data extraction from documents, translation and content creation with human review.
What is the difference between generative AI and ChatGPT?
Generative AI is the broad category of models that create content. ChatGPT is one product built on generative language models. Many other models and applications exist.
How can generative AI be made more accurate?
By grounding responses in retrieved, approved information, constraining output formats, validating results, evaluating on real tasks and routing uncertain or high-impact outputs to people.
Is generative AI secure for company data?
With appropriate architecture, yes. This includes enterprise model agreements, private hosting where required, permission-aware retrieval, data masking and monitoring.
Will generative AI replace writers and analysts?
It changes their work by accelerating drafts and research, while people remain responsible for accuracy, judgement, originality and final decisions.
Next step
Exploring Generative AI?
Tell us what you want to achieve. We will help you judge whether this technology is the right fit and how to test it with your own data.