Implementing GenAI in an enterprise works best when you start with one narrow, measurable use case, connect a hosted model to your own data through retrieval, and build an evaluation suite before you build the interface. Most failed projects skipped one of those three. They chose a vague goal, tried to fine-tune too early, or had no way to tell whether answers were getting better or worse.
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This post walks through the steps we follow when we build GenAI features for clients: picking the use case, preparing data, choosing between prompting, retrieval and fine-tuning, deploying safely, and the problems that usually come up.
What counts as a GenAI solution in an enterprise?
Any system that uses a generative model to produce text, code, images or structured data inside a business process. In practice, most enterprise GenAI work is retrieval-augmented assistants over internal documents, drafting and summarisation tools, data extraction from unstructured files, coding assistance and LLM agents that complete multi-step tasks with tools.
- Customer service: drafting replies for agents, answering from a knowledge base, routing tickets.
- Software development: code suggestions, documentation, test generation.
- Operations: extracting fields from contracts, invoices or forms into your systems.
- Analytics: natural-language questions over data, report summaries.
- Content: product descriptions and first drafts for marketing, with human review.
How do you pick the first use case?
Pick one where the output is easy to check, the data already exists, a mistake is cheap to catch, and success can be measured in a number such as handling time or extraction accuracy. Internal-facing tools with a human reviewing the output are usually the safest starting point.
- Is there a clear owner and a business metric?
- Is the data available, and are you allowed to use it?
- Can a person review outputs before they reach a customer?
- Which regulations apply, such as the EU AI Act or sector rules?
How should you prepare your data?
For most GenAI features you don’t need a training dataset. You need clean, current, well-permissioned documents that a retrieval system can search. Audit the sources, remove duplicates and stale versions, record who’s allowed to see what, and build a small labelled test set of real questions with correct answers.
That test set is the most valuable thing you’ll create. It lets you compare prompts, models and retrieval settings on evidence rather than impressions. If you need help building it at volume, our data labelling team does this work, and our data engineering service covers the pipelines.
Prompting, retrieval or fine-tuning: which approach should you use?
Start with a hosted foundation model and good prompts. Add retrieval-augmented generation when answers depend on your own documents. Consider fine-tuning only when you need a consistent format or style that prompting can’t deliver, or when cost or latency at volume justifies a smaller tuned model. Training a model from scratch is rarely worth it.
| Approach | Use it when | Watch out for |
|---|---|---|
| Prompting a hosted model | The task is general and doesn’t need private data | Data handling terms of the provider |
| Retrieval-augmented generation (RAG) | Answers must come from your documents and stay current | Access control at retrieval time, document quality |
| Fine-tuning | You need a fixed format or style, or a cheaper model at scale | Needs good training examples; must be redone as data changes |
| Open-weight models self-hosted | Data can’t leave your environment | You carry hosting, scaling and patching |
What does deployment involve?
Connecting the model to your systems through APIs, applying your access controls, adding logging and monitoring, and training the people who will use it. Ship to a small group first, measure against your test set and real usage, then widen access. Keep a person in the loop wherever an error would reach a customer.
- Integration with existing systems through APIs or middleware.
- Authentication and per-user permissions applied to retrieved data.
- Logging of prompts, sources and outputs for audit and debugging.
- Cost and latency monitoring, with limits.
- Evaluation runs on every prompt or model change.
- Training and clear guidance for users on what the tool is for.
For production operations at scale, see our MLOps and AI infrastructure service.
What problems come up most often?
Data access and privacy, integration with older systems, inaccurate answers, and adoption. Most are organisational rather than technical. Agree data handling rules with security and legal before the build, plan for awkward integrations, measure accuracy continuously, and give users a reason to switch.
- Privacy and security: clear data policies, encryption, access control and, where needed, private deployment. Test for prompt injection. Our post on enterprise AI security covers the controls.
- Integration: start with systems that have documented APIs, keep the architecture modular and agree who supports what after launch.
- Accuracy: human review for high-stakes outputs, source citations in answers, and regular evaluation runs.
- Adoption: an executive sponsor, training, and honest answers to staff concerns about their jobs.
How can Dignep help?
We build GenAI features, RAG systems and LLM agents with evaluation suites from the start, on AWS, Google Cloud or Supabase. A proof of concept or MVP over 4–8 weeks is usually the right first step. For ongoing work, a dedicated team can be ready in 2–4 weeks. See our GenAI solutions and AI agent development pages.
If your problem is fully solved by an off-the-shelf tool, such as a coding assistant or a meeting summariser, buy that instead of building.
Frequently asked questions
How do we make GenAI outputs reliable?
Ground answers in your documents through retrieval, show sources, keep humans reviewing high-stakes outputs, and run an evaluation set on every change so regressions show up before users see them.
What skills does our team need?
Data engineering, LLM application engineering including prompting and retrieval, evaluation, MLOps and someone who owns the business process. Staff augmentation can fill gaps while your team learns.
How do we handle regulation?
Involve legal and compliance early, document how the system makes decisions, keep audit trails and check obligations under the EU AI Act and sector rules. Our AI governance and security work covers this.
Related reading: GenAI MVP development in 4–8 weeks and AI agent development from PoC to production. Book a free 30-minute call to talk through your use case.
