A Practical Guide to Integrating AI Into Your Existing Product

The reflex problem

When a client asks us to “add AI” to their product, the first question we ask is: what problem are you actually trying to solve?

Nine times out of ten, the answer reveals that AI isn’t the constraint. The problem is a missing feature, an awkward workflow, or data that isn’t structured well enough to be useful. AI can’t fix those things — it just makes them more expensive.

That said, there are genuinely good reasons to integrate AI into a product. The trick is knowing which category you’re in.

Three legitimate use cases

After integrating AI into a dozen products, we keep seeing the same three patterns where it actually earns its place:

Summarisation and extraction.

Taking unstructured input — emails, support tickets, documents, call transcripts — and pulling out structured, actionable information.

Assisted generation.

Helping users produce first drafts, suggestions, or completions faster.

Classification and routing.

Categorising inputs and triggering the right workflow.

If your use case doesn’t fit one of these, be honest with yourself about whether you need AI at all.

Model selection: stop defaulting to GPT-4

GPT-4 is a remarkable model. It’s also overkill for most tasks.

Rough heuristic:

• Simple tasks → cheaper, faster models
• Complex reasoning → higher-tier models
• Stable tasks at scale → fine-tuned smaller models

We’ve seen teams cut inference costs by 80% just by being deliberate here.

Retrieval-Augmented Generation (RAG)

If you want your AI to answer questions about your internal data, you likely need RAG — not fine-tuning.

Store content in a vector database → retrieve relevant chunks → pass to the model as context.

The quality depends on chunking and retrieval. Get those right first.

Prompt engineering is real engineering

Treat prompts like code:

• Specify output format
• Assign a role
• Provide examples
• Define failure behaviour

Version them. Test them. Don’t let them drift.

Evaluation before shipping

Build an eval suite before releasing an AI feature.

Without evals, small prompt changes can silently break things.

This is what separates reliable AI features from fragile ones.

The right mental model

AI output is probabilistic, not deterministic.

Design for hallucinations, wrong formats, and timeouts.

AI should be a carefully constrained collaborator — not a magic layer over broken workflows.