Knowledge hard to find
Staff spend significant time searching policies, manuals, past work and documentation.
04 / Hybrid Intelligence
Generative AI and Large Language Models
Large language models can draft, summarise, explain, translate and reason over text with remarkable fluency. Turning that fluency into dependable business value requires grounding in your data, careful evaluation, security controls and interfaces that keep people in charge. We build generative AI applications with all of that in place.
At a glance
- Service
- Generative AI and Large Language Models
- Pillar
- Hybrid Intelligence
- Typical starting point
- Use case selection and evaluation design
- Typical first phase
- 6 to 12 weeks for a first production use case
- Key technologies
- Generative AI, Large Language Models, Natural Language Processing
Durations depend on scope, data readiness and integration needs.
What is Generative AI and LLMs?
Short answer
Generative AI refers to AI models that create new content such as text, code, images or audio based on patterns learned from data. Large language models (LLMs) are generative models trained on very large text corpora that can understand instructions and generate language. Generative AI development is the engineering of applications that use these models, typically combined with retrieval of organisational knowledge, structured prompts, evaluation, guardrails and integration with business systems.
Most business uses of generative AI fall into a few patterns: assistants that answer questions from company knowledge, copilots that help people draft or analyse within their tools, content and document generation, extraction and transformation of information, and components within automated workflows or agents.
The hard parts are rarely the model call. They are selecting the right use cases, preparing and securing the knowledge the model uses, designing retrieval that finds the right information, evaluating quality on real tasks, preventing data leakage and prompt injection, controlling cost and building interfaces that show sources and let people verify results. We treat each of these as a first-class engineering concern.
Business problems
What problems does Generative AI and LLMs solve?
These are the situations where organisations most often find this service worthwhile.
Time-consuming drafting
Reports, proposals, responses and documentation take hours to write from scratch.
Unmanaged use of public AI tools
Employees use consumer AI tools with company data because no approved option exists.
Pilots with unreliable answers
Early chatbots give confident but incorrect answers, damaging trust.
Multilingual communication needs
Content must be understood or produced in several languages.
Information locked in unstructured text
Valuable information in emails, reports and transcripts is not usable by systems.
Use cases
Where can Generative AI and LLMs be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Enterprise knowledge assistants | Answering employee questions from policies, procedures and technical documentation with citations. |
| Customer support copilots | Suggesting responses and summarising cases for service agents. |
| Document generation | Drafting proposals, reports, specifications or letters from structured inputs for review. |
| Software development | Code assistance, test generation and documentation within engineering workflows. |
| Sales and marketing | Personalised first drafts and content adaptation under brand and compliance review. |
| Education | Tutoring support, question generation and feedback drafts reviewed by educators. |
Who should consider it
- Organisations with large bodies of internal knowledge
- Teams spending significant time drafting or summarising
- IT leaders needing secure, approved generative AI tools
- Product teams adding generative features to software
How AARHIT approaches it
How we deliver Generative AI and LLMs
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Use case selection
We identify and prioritise use cases by value, feasibility, risk and data readiness.
Output: prioritised use case shortlist.
Stage 2 of 6
Knowledge and data preparation
Sources are identified, cleaned, access-controlled and structured for retrieval, with ownership assigned.
Output: prepared knowledge base with access rules.
Stage 3 of 6
Evaluation design
Representative questions and tasks with expected answers are created with subject experts.
Output: evaluation set and quality criteria.
Stage 4 of 6
Architecture and build
Retrieval, prompting, model selection, guardrails, logging and user interface are built and integrated.
Output: working application in a test environment.
Stage 5 of 6
Security and quality testing
We test answer quality, grounding, prompt injection resistance, data leakage and access control.
Output: test results and remediation.
Stage 6 of 6
Launch and improvement
Release to users in stages with feedback capture, usage analytics, cost monitoring and regular evaluation.
Output: live application with improvement cycle.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Models | GPT, Claude, Gemini and other commercial models; Llama, Mistral, Qwen and other open-weight models |
| Retrieval | Vector databases such as pgvector, Qdrant or Azure AI Search, hybrid search, re-ranking |
| Orchestration | LangChain, LlamaIndex, Semantic Kernel or lightweight custom code |
| Customisation | Prompt design, structured outputs, fine-tuning with LoRA where justified |
| Guardrails | Input and output filtering, PII detection, policy checks, citation validation |
| Operations | LLM observability, evaluation pipelines, cost dashboards |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Faster access to accurate organisational knowledge
- Reduced time on first drafts and summaries
- Secure, approved alternatives to consumer AI tools
- Answers users can verify through cited sources
- Measured quality and controlled operating cost
How long does implementation generally take?
Typical timeline
A first production generative AI use case, such as a knowledge assistant over defined sources, typically takes 6 to 12 weeks. Complexity grows with the number of sources, access rules, languages and integrations.
What determines complexity?
- Volume, variety and quality of knowledge sources
- Document-level access permissions
- Accuracy expectations and consequences of errors
- Languages required
- Integration into existing tools and workflows
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Knowledge quality determines answer quality
Outdated, contradictory or duplicated documents produce poor answers. Content ownership and curation matter.
Respect existing permissions
Assistants must not reveal information users are not entitled to see. Retrieval must enforce access rules.
Choose fine-tuning carefully
Most business use cases are better served by retrieval and good prompts. Fine-tuning is useful for style, format or specialised tasks.
Manage expectations
Generative AI is powerful but fallible. Users need guidance on appropriate use and verification.
| Risk to consider | How we manage it |
|---|---|
| Fabricated or incorrect answers | Retrieval grounding, citation requirements, refusal when evidence is missing and evaluation. |
| Leakage of confidential data | Permission-aware retrieval, data classification, PII controls and enterprise model agreements. |
| Prompt injection | Separating instructions from retrieved content, output validation and restricted tool access. |
| Copyright and content risks | Clear policies on generated content, source attribution and human review for publication. |
| Uncontrolled costs | Model tiering, caching, usage limits and cost monitoring. |
FAQ
Questions about Generative AI and LLMs
What is retrieval-augmented generation (RAG)?
RAG is a technique where relevant information is first retrieved from approved sources, such as documents or databases, and then provided to a language model to generate an answer based on that information, often with citations. It reduces fabricated answers and keeps responses current.
Should we fine-tune an LLM on our data?
Usually not as a first step. Retrieval with good prompting handles most knowledge use cases more cheaply and keeps information up to date. Fine-tuning is worthwhile for specialised formats, tone, or tasks where evaluation shows clear benefit.
Is our data safe when using generative AI?
It can be, with the right architecture: enterprise model agreements that exclude training on your data, private networking or self-hosted models where needed, permission-aware retrieval, data masking and logging.
Can generative AI work in Hindi and other Indian languages?
Leading models handle Hindi and several Indian languages, with varying quality. We evaluate candidate models on your languages and tasks before recommending one.
How do you stop an AI assistant from making things up?
By grounding answers in retrieved sources, instructing and validating that answers stay within them, requiring citations, allowing the assistant to say it does not know and continuously evaluating quality.
Can we use open-source LLMs?
Yes. Open-weight models can be hosted in your cloud or data centre for data control or cost reasons. Their quality for your tasks should be compared with commercial models using an evaluation set.
Next step
Considering Generative AI and LLMs for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.