How to Add AI to an Existing Development Workflow
A practical beginner-friendly guide: choose your first LLM, add AI to your current stack, and use it safely in real codebases without breaking architecture.
Firstly, if you are just starting with AI in software development, confusion is normal: which model to choose, where to begin, and how to avoid breaking an existing project. Secondly, most teams need practical decisions, not abstract theory. Therefore, this guide is intentionally practical, so you can apply it immediately in a real team workflow.
What this guide solves
Beginners usually ask the same three questions. For example, they ask which model to pick first and where to integrate it. In addition, they want to know how to keep quality stable in legacy code. The table below gives the short answer first, and then shows what it means in day-to-day work.
| Question | Short answer | Practical meaning |
|---|---|---|
| Which LLM should I choose? | Start with one model and one use case. | Use it for PR summaries and code explanations first. |
| How do I add AI to my current stack? | Add AI as a helper layer, not as a core dependency. | Use feature flags and one central AI service. |
| How do I use AI in legacy code? | AI writes drafts, humans approve. | Keep the same review and testing standards. |
1) Pick one goal and one metric
However, the most common beginner mistake is trying to apply AI everywhere at once. Instead, pick one repetitive task and measure one outcome.
Simple starter example:
- Task: writing PR descriptions currently takes 20 minutes.
- Goal: reduce to 8-10 minutes.
- Metric: average time over 10 PRs before vs after AI support.
Overall, if you cannot measure it, you cannot tell whether AI actually helped.
2) Choose a tool without overthinking
As a beginner, do not optimize for perfection. In addition, avoid switching tools every day. Choose a "good enough" setup and start using it.
| Option | Best for | Beginner recommendation |
|---|---|---|
| AI chat tool | Code explanations, draft text, planning | Best first step |
| IDE plugin | Autocomplete and refactor drafts | Add after week 1-2 |
| API integration | Automated product flows | Only after use case is proven |
Copy-ready starter prompts:
1) "Explain this file for a junior developer:
- main responsibility
- key dependencies
- 2 possible risks
Context: [YOUR_CONTEXT]"
2) "Draft tests for this bugfix.
Do not change business logic.
Context: [YOUR_CONTEXT]"
3) "Write a PR description from this diff:
- what changed
- why it changed
- how to test
- risks
Context: [YOUR_CONTEXT]"3) Add AI without rebuilding everything
Furthermore, you can introduce AI into an existing PHP + Docker setup incrementally. Keep it simple and reversible.
- Add AI config into
.env. - Create one central AI service for calls.
- Guard features with
AI_ENABLED=true/false. - Log where AI was used and whether output was merged.
Example .env:
AI_ENABLED=true
AI_MODEL=mini
AI_USECASE_PR_SUMMARY=trueNeed this setup working in your team this month?
We can help define your first use cases, prompts, guardrails, and rollout checklist.
Explore AI solutions →4) Practical examples in existing codebases
Meanwhile, these are low-risk, high-value AI use cases for beginners:
| Situation | AI output | Human validation |
|---|---|---|
| Large legacy file | Responsibility summary and risk points | Check against actual runtime behavior |
| Bugfix | Test draft and edge-case ideas | Verify test relevance and coverage |
| Pull request | Change summary and test plan | Ensure technical accuracy |
5) Safety rules for real projects
Use this as a simple team policy:
| Do this | Avoid this |
|---|---|
| AI drafts, humans approve. | Direct merge without validation. |
| Keep standard tests and CI checks. | Exceptions for AI-generated code. |
| Start with small, reversible tasks. | Big rewrites from day one. |
Finally, this way AI improves speed while your quality bar stays unchanged.
6) A 14-day rollout plan
- Day 1: choose 2 use cases and baseline timing.
- Days 2-4: test starter prompts on real tasks.
- Days 5-7: keep only prompts that produce value.
- Days 8-11: write one shared team guideline.
- Days 12-14: measure impact and lock final workflow.
On the other hand, if your team is still mapping process basics, compare this with your design workflow process as well: How to create graphic materials for your business.
FAQ for beginners
Do I need to be an AI expert first? +
No. Start with one simple use case and a clear prompt structure. Moreover, consistency matters more than advanced tricks.
Should I rebuild my architecture before adding AI? +
No. Add AI around your existing flow first, then decide whether deeper integration is worth it. Therefore, risk stays lower during rollout.
What if AI gives a wrong answer? +
Treat AI output as a draft. Validate with the same review, tests, and CI quality gates as human-written code. However, do not skip manual verification for edge cases.
When should we get external help? +
If progress is still unclear after 2-3 weeks, a focused workshop can help establish process, prompts, and team standards quickly.
Next step
Want a practical AI onboarding plan for your team?
We can build a simple, measurable rollout based on your existing stack and real tasks.
Book a consultation →SIA DESIGN
Design and web development
The SIA DESIGN team writes practical guides on web design, development and SEO.
Loe rohkem SIA DESIGN meeskonnast →
