jaisravi.dev
Leveraging LLMs and generative AI for smart application features.
I use AI not as a hype feature, but as a practical tool to make applications smarter and more useful. My focus is on integrating LLMs and generative AI in a way that improves real user workflows — whether it's content generation, smart suggestions, summarization, or automation.
Instead of just calling an AI API, I think about how the model fits into the overall system architecture, how data flows securely, and how responses are handled efficiently.
| Area | Tools & Concepts |
|---|---|
| LLM Integration | OpenAI APIs, Prompt Engineering, Gemini |
| Backend Integration | Node.js AI Controllers, API-Based Model Calls |
| Context Handling | Structured Prompts, Role-Based Prompt Design |
| Data Handling | Input Validation, Response Parsing, JSON Schema Enforcement |
| Performance | Token Optimization, Response Streaming (SSE Basics) |
| Deployment | Secure API Key Management, Cloud-Based Integration |
When integrating AI, I first define the exact problem the model needs to solve. I avoid vague prompts and instead design structured prompts with clear instructions and expected output formats.
I build a proper backend layer between the frontend and the AI model to control validation, rate limiting, and response formatting. This keeps the system secure and predictable. I also consider fallback logic in case the AI response fails or needs refinement.
AI responses can be slow or expensive if not handled properly. I focus on optimizing token usage and minimizing unnecessary model calls.
Where needed, I use streaming approaches (like Server-Sent Events) for a better user experience, so users aren't left waiting at a loading screen. I also design AI features so that they can scale without overwhelming backend resources. The goal is to keep AI features responsive and cost-efficient.
I never expose API keys on the frontend. All AI calls are handled securely through backend services.
I validate user input before sending it to the model to avoid prompt injection and misuse. Sensitive data is never directly injected into prompts without proper control. Security and cost control go hand in hand in AI-based systems.
AI integration should enhance the product experience — not complicate the architecture.