Integrating AI into your product: a pragmatic guide without the hype

The question nobody asks

Before any discussion about LLMs, agents, or RAG, there's a more important question:

What concrete problem does AI solve better than a deterministic solution?

If the answer is vague or is "because everyone's doing it" — stop there.

Use cases where AI brings real value

AI is justified when the input is unstructured or ambiguous:

• Document processing: extracting data from contracts, invoices, letters. A regex parser doesn't cut it, AI does.
• Semantic classification: support tickets, user feedback, articles — categories that can't be covered with simple rules.
• Contextual content generation: response drafts, long document summaries, personalized explanations.
• Semantic search: when the user searches for "something related to returns in the summer of last year" and doesn't know the exact terms.
• Conversational assistant: when the user needs to navigate a complex application or find information in their own data.

Use cases where AI is an unnecessary complication

• You're calculating a product price → deterministic math, not AI
• You're validating an email → regex, not AI
• You're filtering data by fixed criteria → SQL, not AI
• You're checking if a user has permissions → access control logic, not AI

How to evaluate the real cost

The cost of an LLM integration isn't just the API price:

1. Cost per request × anticipated volume = direct monthly cost
2. Latency — an LLM call takes 1-5 seconds. Is this tolerable in your flow?
3. Accuracy — what happens when AI is wrong? Is it detectable? Reversible?
4. Maintenance complexity — prompt engineering, model updates, evaluation framework

Responsible implementation strategy

Step 1: POC with real data Test on 100-500 real production examples. Not artificial examples. Measure accuracy against a human baseline.

Step 2: Mandatory fallback Every AI integration must have a fallback: if AI fails or returns something invalid, the system must continue functioning.

Step 3: Logging and continuous evaluation Log all AI inputs and outputs. Periodically review failures. Accuracy degrades over time on real data.

Step 4: Cost monitoring Implement alerts for daily cost. A production bug sending 10,000 extra requests can cost hundreds of euros.

Which models to use

I have no commercial affiliation, so I can say directly:

• GPT-4o / Claude 3.5 Sonnet: for complex tasks requiring reasoning. Higher cost, better quality.
• GPT-4o mini / Claude Haiku: for classification, simple extraction, large volumes. Excellent cost/quality ratio.
• Local models (Llama, Mistral): if data is sensitive and can't leave your infrastructure. Significant operational overhead.

Conclusion

AI is a tool, not a strategy. Companies that win with AI aren't those that "adopt AI" — they're those that identify the right problem, measure results, and iterate on real data.

If you have a concrete problem and want to evaluate whether AI solves it efficiently, we're happy to do a quick assessment together.