Chapter 2: Building AI-Powered Apps

Architecture and Patterns

Building robust AI applications requires understanding common patterns, error handling, and scaling considerations.

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Common Architecture Patterns

1. Simple Request-Response

Best for: Simple queries, chatbots

2. RAG (Retrieval Augmented Generation)

Best for: Knowledge bases, documentation assistants

3. Agent Architecture

Best for: Complex tasks, autonomous systems

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Prompt Management

Organizing Prompts

PROMPTS = {
    "summarize": """Summarize the following text in {length} sentences:
    {text}""",
    
    "translate": """Translate from {source} to {target}:
    {text}"""
}

Versioning

• Track prompt versions
• A/B test different prompts
• Document what works

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Error Handling

Common Errors

Error	Cause	Solution
Rate limit	Too many requests	Implement backoff
Timeout	Slow response	Increase timeout, retry
Context length	Input too long	Truncate or chunk
Invalid response	Model issues	Validate, retry

Robust Implementation

def get_ai_response(prompt, retries=3):
    for attempt in range(retries):
        try:
            response = call_api(prompt)
            if validate_response(response):
                return response
        except (TimeoutError, RateLimitError) as e:
            if attempt == retries - 1:
                raise
            time.sleep(2 ** attempt)
    return fallback_response()

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Scaling Considerations

Caching

• Cache repeated queries
• Store embeddings for similarity search
• Reduce costs and latency

Async Processing

• Queue long requests
• Process in background
• Notify on completion

Load Balancing

• Multiple API keys
• Fallback providers
• Geographic distribution

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Key Takeaways

✅ Choose architecture based on use case

✅ Manage prompts systematically

✅ Implement robust error handling

✅ Plan for scale from the start

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Next Steps

Continue to Chapter 3: No-Code/Low-Code Solutions.