Building with Generative AI

This series is a practical look at building with generative AI. The goal is simple: explain the core ideas clearly, show the trade-offs, and use TypeScript with the Vercel AI SDK where code helps.

Why Generative AI Matters

Generative AI is changing how we design and build software. The useful part is not the hype. It is that we can now add language, reasoning, summarisation, retrieval and tool-use patterns to normal products.

  • Better User Experience: Instead of rigid, scripted interactions, we can build flows that understand intent, ask follow-up questions and help users get to an outcome faster.

  • Workflow Support: Models can summarise information, draft content, classify inputs, generate structured data and help users move through multi-step workflows.

  • Personalisation: AI can adapt explanations, recommendations and interfaces based on context, preferences and previous behaviour.

  • New Product Patterns: Natural-language database queries, retrieval over private documents, guided assistants and tool-using agents are now practical enough to prototype and ship carefully.

  • Engineering Leverage: Used well, AI features can reduce manual work and make products feel more responsive. Used poorly, they create cost, security and trust problems. The difference is engineering discipline.

Not every AI feature needs agents, fine-tuning or a complicated architecture. Most good systems start small, measure what matters and add complexity only when it pays for itself. Before the practical articles, it helps to look at the main ways to improve an LLM-powered system.

The LLM Improvement Progression

The LLM Improvement Progression below shows the practical steps you can take to improve an AI system. Start with the simple options, measure the output, then move to heavier techniques only when the problem calls for it.

What Each Step Means

Zero-Shot: The model is given a prompt and expected to perform the task without any examples. This is the simplest and fastest approach, relying entirely on the model's pre-trained knowledge.

Few-Shot: The prompt includes a few examples of the task, helping the model understand the desired pattern or format. This improves accuracy for tasks with specific requirements.

Chain of Thought: The model is encouraged to reason step-by-step, often by prompting it to "think out loud." This is especially useful for complex reasoning or multi-step problems.

Temperature: Adjusting the temperature parameter controls the randomness of the model's output. Lower values make responses more deterministic, while higher values increase creativity and diversity.

Workflows: Combining multiple LLM calls or steps to solve more complex tasks. Each step may handle a different part of the problem, passing results along a pipeline.

Evaluators: Using automated checks or even another LLM to evaluate and score outputs, ensuring quality and consistency before presenting results to users.

Agentic Loops: Allowing the LLM (or a set of LLMs) to act autonomously, plan, and adapt based on feedback, often iterating until a goal is achieved.

LLM Routers: Directing queries to specialized models or prompt templates based on the type or complexity of the input, improving efficiency and accuracy.

Fine-Tuning: Training the model further on your own data to specialize it for your domain or use case, resulting in more relevant and higher-quality outputs.

Sampling: Generating multiple candidate outputs, then selecting the best one. This can be useful for creative or open-ended tasks.

Real-World Applications

E-commerce

  • Product recommendations
  • Customer support chatbots
  • Dynamic pricing
  • Content generation

Healthcare

  • Medical documentation
  • Patient education
  • Diagnostic assistance
  • Research analysis

Education

  • Personalized learning
  • Content creation
  • Student assessment
  • Tutoring systems

Finance

  • Risk assessment
  • Market analysis
  • Customer service
  • Fraud detection

Where This Is Going

The space is moving quickly, but a few trends are worth watching:

  • Multimodal AI: Systems that understand text, images, audio, and video
  • Edge AI: AI processing on devices for better privacy and performance
  • Specialized Models: Domain-specific models for industries
  • AI Agents: More sophisticated autonomous systems
  • Democratization: Easier access to AI capabilities for developers

Getting the Most Out of This Series

Each article builds on the previous ones, so I recommend going through them in order. You'll find:

  • Practical Examples: Real code you can run immediately
  • Best Practices: Lessons learned from building production AI systems
  • Common Pitfalls: Things to avoid when working with AI
  • Performance Tips: How to optimize your AI applications
  • Security Considerations: Keeping your AI systems safe

Whether you're building your first AI feature or improving an existing system, the main skill is knowing which pattern fits the job.

Technology Stack

This series focuses on practical implementation using:

Vercel AI SDK

  • Streamlined AI development
  • Built-in streaming and error handling
  • Easy integration with Next.js
  • Support for multiple AI providers

OpenAI Node.js Library

  • Direct access to OpenAI's models
  • Fine-grained control over API calls
  • Advanced features like fine-tuning
  • Comprehensive TypeScript support

TypeScript

  • Type safety for AI interactions
  • Better developer experience
  • Reduced runtime errors
  • Improved code maintainability

Getting Started

Before diving into the articles, make sure you have:

# Create a new Next.js project with AI SDK
npx create-next-app@latest my-ai-app --typescript --tailwind --app

# Install Vercel AI SDK
npm install ai

# Install OpenAI SDK
npm install openai

# Set up environment variables
echo "OPENAI_API_KEY=your_api_key_here" > .env.local

Key Concepts

Prompts and Context

Understanding how to craft effective prompts is crucial for getting the best results from AI models. We'll explore techniques for writing clear, specific, and context-rich prompts.

Vector Embeddings

Embeddings convert text into numerical representations that capture semantic meaning. They're essential for building search, recommendation, and RAG systems.

Retrieval-Augmented Generation

RAG combines the power of large language models with external knowledge sources, enabling AI systems to provide accurate, up-to-date information.

Fine-tuning

Customizing pre-trained models for specific tasks and domains can significantly improve performance and reduce costs.

AI Agents

Autonomous AI systems that can plan, execute, and adapt to achieve complex goals.

Best Practices

Security and Privacy

  • Never expose API keys in client-side code
  • Implement proper rate limiting
  • Sanitize user inputs
  • Consider data privacy regulations

Performance Optimization

  • Use streaming for better user experience
  • Implement caching strategies
  • Optimize prompt length and complexity
  • Monitor API usage and costs

Error Handling

  • Graceful degradation when AI services are unavailable
  • Clear error messages for users
  • Retry logic for transient failures
  • Fallback mechanisms