Integrating generative AI into a software product

Generative AI gives software products new abilities through models that produce text, code, images and summaries. But adding a chat box to every product does not create value. Successful integrations noticeably speed up or simplify a specific user task.

Use cases that create value

• Summarising: long documents, meeting notes, customer requests
• Drafting: emails, reports, product descriptions, code
• Classification and extraction: reading data from invoices, tagging tickets
• Natural-language search and Q&A: over company documents and product data
• Agent-like workflows: automating multiple steps with user approval

First find the repetitive task users spend the most time on; see our customer interview guide.

Choosing models

• Hosted models via API give a fast start.
• Test task and language performance (including Turkish) on your own samples.
• Balance cost, speed and quality per task; small models may be enough for simple ones.
• Design to reduce dependence on a single provider.

For overall strategy, see our AI startup guide.

Context and data

Correct answers depend on correct context. Retrieving the user's documents or product data and giving it to the model (retrieval-augmented generation) reduces made-up answers. Respect permissions: users should only get answers from data they are allowed to see.

Measuring quality

• Build an evaluation set from real usage examples.
• Test against it after every model or prompt change.
• Collect user feedback (thumbs up/down).
• Require human approval for critical decisions.

Cost and performance

Every request costs money. Caching, short focused prompts and the right model reduce cost. Reflect usage costs in pricing; see our cloud cost guide.

Security and privacy

• Know which provider and country personal and customer data goes to; see our KVKK guide.
• Secure contractually that customer data is not used for model training.
• Limit inputs and the tools the model can access to defend against new attacks such as prompt injection.

User trust

Label AI-generated content clearly, cite sources and let users edit.

Conclusion

Matched to the right problem, generative AI noticeably strengthens your product. Start with a small, measurable feature and expand as you measure quality.