AI Integration in Modern Web Applications | Chowdhury Tafsir Ahmed Siddiki

Bringing AI Into Production Applications

AI is no longer experimental -- it is a core feature in modern applications. At Standard Insights, I work extensively with multiple AI providers to build intelligent features that directly impact user experience.

Working with Multiple AI Providers

Each AI provider has strengths that suit different use cases:

Claude (Anthropic):

- Excellent for long-form content analysis and generation

- Strong reasoning capabilities for complex decision-making

- Used at Standard Insights for data analysis workflows

OpenAI (GPT):

- Versatile for a wide range of text generation tasks

- Strong function calling capabilities for structured outputs

- Used for conversational features and content summarization

Gemini (Google):

- Great for multimodal tasks involving text and structured data

- Used in Career Dock for resume optimization and career suggestions

- Cost-effective for high-volume processing

Integration Architecture

Integrating AI into a web application requires thoughtful architecture:

Vercel AI SDK is my preferred tool for streaming AI responses in Next.js applications. It provides:

- Server-sent events for real-time response streaming

- React hooks for managing AI conversation state

- Provider-agnostic interface that works with OpenAI, Claude, and others

- Built-in error handling and retry logic

Handling AI in Production

Production AI integration comes with unique challenges:

Latency Management: AI API calls can take seconds. I use streaming responses so users see output immediately rather than waiting for the full response.

Cost Control: AI API calls are expensive at scale. I implement caching for repeated queries, rate limiting per user, and choosing the right model size for each task.

Error Handling: AI APIs can fail or return unexpected results. Robust error handling with fallbacks ensures the application degrades gracefully.

Queue-Based Processing: For heavy AI tasks, I use BullMQ to process requests asynchronously. The user submits a request, and the result is delivered when ready.

Prompt Engineering in Practice

Writing effective prompts is as important as the code itself:

System prompts define the AI's role, constraints, and output format

Few-shot examples improve consistency for specific tasks

Structured output parsing ensures AI responses match expected schemas

Iterative refinement based on real user feedback and edge cases

Real-World AI Features I Have Built

At Standard Insights:

- Data analysis pipelines that process business data and generate actionable insights

- Content generation workflows with human-in-the-loop review

- Automated report generation combining data queries with AI summarization

In Career Dock:

- AI-powered resume suggestions tailored to specific job postings

- LinkedIn profile optimization recommendations

- Cover letter generation based on user profile and job description

The Future of AI in Web Development

AI is becoming a standard tool in the developer's toolkit. The developers who understand both the capabilities and limitations of these models will build the most impactful applications. My focus is on using AI to solve real problems -- not just adding it as a feature checkbox, but integrating it where it genuinely improves the user experience.