AI APIs use panna coding theriyanum aa?

Basic coding therinjaa podhum! REST API call panna therinjaa AI APIs easy aa use pannalam. Python or JavaScript recommend.

AI API calls cost evlo?

Most APIs pay-per-use. OpenAI GPT-4o roughly $2.50/1M input tokens. Small apps ku monthly $5-20 range la varum. Free tiers available!

Which AI API best for beginners?

OpenAI API – best documentation, largest community, easiest integration. Google Gemini API also good with generous free tier.

AI app build panna backend vendumaa?

Simple apps ku serverless functions (Vercel, Netlify) podhum. Complex apps ku proper backend (Node.js, Python) recommend.

Rate limiting epdi handle pannuvadhu?

Queue system use pannunga, retry with exponential backoff, user-level rate limits set pannunga, and caching implement pannunga.

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Building apps using AI + APIs

Intermediate⏱ 14 min read📅 Updated: 2026-02-17

🚀 Introduction – AI-Powered Apps Era

2026 la every app AI-powered aa maarudhu! 🌊

AI APIs use panna nee build pannalam:

🤖 Chatbots – Customer support, personal assistants
📝 Content generators – Blog posts, product descriptions
🖼️ Image tools – Generation, editing, analysis
🔍 Smart search – Semantic search, recommendations
🗣️ Voice apps – Transcription, text-to-speech
📊 Data analysis – Insights from unstructured data

The best part? Nee ML expert aa irukkanum nu illae! API call panna therinjaa podhum! 😎

Old Way	New Way (AI APIs)
ML team hire pannanum	API call mattum
Months of training	Minutes to integrate
GPU infrastructure	Pay per request
PhD required	Any developer can build

Let's build some AI-powered apps! 💪

🗺️ AI API Landscape – Know Your Options

2026 la available AI APIs:

🔤 Text/Language APIs:

Provider	Model	Best For	Free Tier
OpenAI	GPT-4o, o3-mini	General purpose	$5 credit
Anthropic	Claude 3.5	Long context, coding	Limited
Google	Gemini 2.0	Multimodal	Generous
Mistral	Mistral Large	European, fast	Yes

🖼️ Image APIs:

Provider	Best For	Free Tier
OpenAI DALL-E 3	Text to image	Limited
Stability AI	Customizable generation	Yes
Google Imagen	High quality	Via Gemini

🗣️ Voice APIs:

Provider	Best For	Free Tier
OpenAI Whisper	Speech to text	API pricing
ElevenLabs	Text to speech	10k chars/month
Deepgram	Real-time transcription	$200 credit

Pro tip: Start with one API – master it, then expand! 🎯

💡 API Key Security – CRITICAL!

💡 Tip

API keys NEVER client-side code la vaikkaadhenga! 🔐

❌ WRONG:

javascript

// Browser code la API key 😱
const response = await fetch('https://api.openai.com/v1/chat', {
  headers: { 'Authorization': 'Bearer sk-abc123...' }
});

✅ RIGHT:

javascript

// Backend/serverless function la API key
// .env file: OPENAI_API_KEY=sk-abc123...
const response = await openai.chat.completions.create({...});

Rules:

1. 🔒 API keys environment variables la store pannunga

2. 🚫 Never commit to git (.gitenv la add pannunga)

3. 🔄 Rotate keys regularly

4. 💰 Spending limits set pannunga (accidentally $1000 bill varaadhu!)

5. 🛡️ Backend proxy through route pannunga

💻 Your First AI API Call – Step by Step

Let's build a simple AI chatbot API:

Step 1: Setup Project 📁

bash

mkdir ai-chat-app && cd ai-chat-app
npm init -y
npm install openai express dotenv

Step 2: Environment Variables 🔐

bash

# .env
OPENAI_API_KEY=sk-your-key-here

Step 3: Basic Server 🖥️

javascript

import OpenAI from 'openai';
import express from 'express';
import dotenv from 'dotenv';
dotenv.config();

const app = express();
app.use(express.json());

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

app.post('/api/chat', async (req, res) => {
  try {
    const { message } = req.body;
    
    const completion = await openai.chat.completions.create({
      model: 'gpt-4o-mini',
      messages: [
        { role: 'system', content: 'You are a helpful assistant.' },
        { role: 'user', content: message }
      ],
      max_tokens: 500,
      temperature: 0.7
    });

    res.json({ 
      reply: completion.choices[0].message.content 
    });
  } catch (error) {
    res.status(500).json({ error: 'AI call failed' });
  }
});

app.listen(3000, () => console.log('Server running! 🚀'));

That's it! 15 lines la oru AI chatbot backend ready! 🎉

🏗️ AI App Architecture Pattern

🏗️ Architecture Diagram

**Production-ready AI app architecture:**

```
┌──────────────────────────────────────────────┐
│              FRONTEND (React/Next.js)         │
│  ┌─────────┐  ┌──────────┐  ┌─────────────┐ │
│  │ Chat UI  │  │ File     │  │ Results     │ │
│  │          │  │ Upload   │  │ Display     │ │
│  └────┬─────┘  └────┬─────┘  └──────▲──────┘ │
└───────┼──────────────┼───────────────┼────────┘
        │              │               │
   ┌────▼──────────────▼───────────────┼────┐
   │           API LAYER (Backend)          │
   │  ┌───────────┐  ┌──────────────────┐   │
   │  │ Rate      │  │ Auth + Validation│   │
   │  │ Limiter   │  │                  │   │
   │  └─────┬─────┘  └────────┬─────────┘   │
   │        │                 │              │
   │  ┌─────▼─────────────────▼──────────┐   │
   │  │        AI SERVICE LAYER          │   │
   │  │  ┌─────────┐  ┌──────────────┐   │   │
   │  │  │ Prompt  │  │ Response     │   │   │
   │  │  │ Builder │  │ Parser       │   │   │
   │  │  └────┬────┘  └──────▲───────┘   │   │
   │  └───────┼──────────────┼───────────┘   │
   └──────────┼──────────────┼───────────────┘
              │              │
   ┌──────────▼──────────────┼───────────┐
   │         AI APIs                      │
   │  ┌─────────┐ ┌────────┐ ┌────────┐  │
   │  │ OpenAI  │ │ Claude │ │ Gemini │  │
   │  └─────────┘ └────────┘ └────────┘  │
   └──────────────────────────────────────┘
              │
   ┌──────────▼──────────────────────────┐
   │         DATA LAYER                   │
   │  ┌─────────┐ ┌────────┐ ┌────────┐  │
   │  │ Cache   │ │Database│ │ Queue  │  │
   │  │ (Redis) │ │(Postgres)│ │(Bull)  │  │
   │  └─────────┘ └────────┘ └────────┘  │
   └──────────────────────────────────────┘
```

**Key components:**
- 🛡️ **Rate Limiter** – API abuse prevent pannunga
- 🔐 **Auth** – User authentication + API key protection
- 📝 **Prompt Builder** – Dynamic prompts construct pannunga
- 📊 **Response Parser** – AI output structured data aa convert pannunga
- 💾 **Cache** – Same questions ku cached responses (cost save!)
- 📬 **Queue** – Long-running AI tasks async handle pannunga

🔧 Prompt Engineering for APIs

API la send panna prompts oda quality = app quality!

System Prompt Design:

javascript

const systemPrompts = {
  customerSupport: `You are a helpful customer support agent 
    for TechStore. Be polite, concise. If you don't know, 
    say "Let me connect you with a human agent."
    Never discuss competitors. Max 3 sentences per reply.`,
    
  codeReviewer: `You are a senior code reviewer. 
    Review code for: bugs, security, performance, readability.
    Format: bullet points with severity (HIGH/MED/LOW).
    Be constructive, suggest fixes.`,
    
  contentWriter: `You are a Tanglish content writer for 
    a tech blog. Write engaging, educational content.
    Use emojis, bold text, and tables.
    Target audience: Indian developers.`
};

Temperature Guide:

Use Case	Temperature	Why
Code generation	0.0 - 0.3	Deterministic, accurate
Customer support	0.3 - 0.5	Consistent but natural
Creative writing	0.7 - 0.9	More variety, creative
Brainstorming	0.9 - 1.0	Maximum creativity

Token Optimization:

🎯 max_tokens set pannunga – unnecessary long responses avoid
📝 System prompt concise aa vainga – every token costs money
🔄 Conversation history trim pannunga – last 10 messages podhum

🎬 Real Project: AI Content Summarizer

✅ Example

Let's build a URL content summarizer:

javascript

app.post('/api/summarize', async (req, res) => {
  const { url, style } = req.body;
  
  // 1. Fetch article content
  const article = await fetchArticle(url);
  
  // 2. Build prompt based on style
  const prompts = {
    bullet: 'Summarize in 5 bullet points',
    tweet: 'Summarize in a tweet (280 chars)',
    eli5: 'Explain like I am 5 years old',
    tanglish: 'Summarize in Tanglish with emojis'
  };
  
  // 3. Call AI API
  const summary = await openai.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [
      { role: 'system', content: prompts[style] },
      { role: 'user', content: article.text }
    ],
    max_tokens: 300
  });
  
  // 4. Cache result
  await cache.set(url + style, summary, '1h');
  
  res.json({ summary: summary.choices[0].message.content });
});

Features: Multiple summary styles, caching, clean API! 🎯

Cost: ~$0.001 per summary with gpt-4o-mini! 💰

💰 Cost Management – Don't Go Broke!

AI API bills quickly escalate aagum – careful! 💸

Cost Calculation Formula:

code

Monthly Cost = (Avg tokens per request × Requests per day × 30) 
               ÷ 1,000,000 × Price per million tokens

Example:

500 tokens/request × 1000 requests/day × 30 = 15M tokens/month
GPT-4o-mini: $0.15/1M input + $0.60/1M output
Monthly cost: ~$11 👍

Cost Optimization Strategies:

Strategy	Savings	Implementation
Caching	40-60%	Same queries cache pannunga
Smaller models	50-80%	gpt-4o-mini vs gpt-4o
Token limits	20-30%	max_tokens restrict pannunga
Batch processing	15-25%	Batch API use pannunga
Prompt optimization	10-20%	Shorter system prompts

Must-do safety measures:

🚨 Spending alerts set pannunga ($10, $50, $100)
🔒 Hard limits set pannunga (monthly max)
📊 Dashboard monitor pannunga daily
🧮 Per-user limits implement pannunga

🔄 Streaming Responses – Better UX

AI responses stream pannaa UX 10x better:

Without streaming: User 5-10 seconds blank screen paarppaaru 😴

With streaming: Text character-by-character appear aagum – ChatGPT maadiri! ✨

Server-Sent Events (SSE) Implementation:

javascript

app.post('/api/chat/stream', async (req, res) => {
  res.setHeader('Content-Type', 'text/event-stream');
  res.setHeader('Cache-Control', 'no-cache');
  
  const stream = await openai.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: req.body.message }],
    stream: true  // 🔑 Enable streaming!
  });
  
  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || '';
    res.write(`data: ${JSON.stringify({ content })}\n\n`);
  }
  
  res.write('data: [DONE]\n\n');
  res.end();
});

Frontend (React):

javascript

const response = await fetch('/api/chat/stream', {
  method: 'POST',
  body: JSON.stringify({ message })
});

const reader = response.body.getReader();
while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  // Append text to UI progressively
  setText(prev => prev + decode(value));
}

User experience difference: Night and day! 🌙☀️

🛡️ Error Handling & Resilience

AI APIs fail aagum – prepare pannunga! 🛡️

Common Failures:

Error	Cause	Solution
429 Rate Limit	Too many requests	Exponential backoff + queue
500 Server Error	API down	Retry + fallback provider
Timeout	Long response	Streaming + timeout limits
Token Limit	Input too long	Truncate + chunk
Content Filter	Blocked content	Handle gracefully

Robust API Call Pattern:

javascript

async function callAI(prompt, retries = 3) {
  for (let i = 0; i < retries; i++) {
    try {
      return await openai.chat.completions.create({
        model: 'gpt-4o-mini',
        messages: [{ role: 'user', content: prompt }],
        timeout: 30000
      });
    } catch (error) {
      if (error.status === 429) {
        // Rate limited – wait and retry
        await sleep(Math.pow(2, i) * 1000);
        continue;
      }
      if (i === retries - 1) throw error;
    }
  }
}

Fallback Strategy:

Primary: OpenAI → Fallback: Claude → Last resort: Cached response

Never let your app completely break because AI API down! 🏗️

⚠️ Common Mistakes Building AI Apps

⚠️ Warning

Avoid these costly mistakes:

1. 💸 No spending limits – Woke up to $500 bill!

2. 🔐 API keys in frontend – Hackers steal within hours

3. 🐌 No caching – Same query 100 times = 100x cost

4. 📝 No input validation – Users send 100K token prompts

5. 🔄 No retry logic – App crashes on first API error

6. 📊 No monitoring – Don't know usage until bill arrives

7. 🎯 Wrong model choice – Using GPT-4o for simple tasks (10x cost!)

8. 🧹 No output sanitization – AI output directly render pannuradhu (XSS risk!)

Cost horror story: Oru developer API key leak aagi, overnight $10,000 bill vandhadhu! Always set hard spending limits! 💀

🚀 Deployment & Scaling

AI app deploy panna best practices:

Deployment Options:

Platform	Best For	Cost	Scaling
Vercel	Next.js apps	Free tier	Auto
Railway	Full stack	$5/month	Easy
AWS Lambda	Serverless	Pay per use	Auto
Fly.io	Global deploy	Free tier	Manual

Scaling Checklist:

✅ Caching layer (Redis) – repeated queries handle
✅ Queue system (Bull/BullMQ) – async processing
✅ CDN – static assets serve
✅ Database – conversation history store
✅ Monitoring – Sentry + custom dashboards
✅ Rate limiting – per-user + global limits

Performance Tips:

🚀 Stream responses – better perceived performance
💾 Cache aggressively – semantic similarity caching
⚡ Use smaller models for simple tasks
🔄 Background processing for heavy tasks
📊 Monitor latency – P95 under 5 seconds target

✅ Key Takeaways

✅ AI APIs use panni apps build panna straightforward — no ML expertise venum illa!

✅ API choice important — OpenAI, Anthropic, Google Gemini — each model ku unique strengths, budgets consider pannunga

✅ Security critical — API keys environment variables la, never client-side code la vaikkaadhenga

✅ Cost management essential — spending limits set, caching implement, smaller models use panni budget control pannunga

✅ Streaming UX improve pannum — user experience drastically better aagum, token-by-token responses display panna

✅ Error handling resilient apps — rate limiting, retries, fallback mechanisms implement pannunga production apps ku

✅ Architecture matters — API layer, inference layer, cache layer separate ah maintain panna independently scale pannalam

✅ Start small, iterate fast — simple use case proof-of-concept pannunga, then features add panni scale pannunga

🏁 Mini Challenge

Challenge: Build Complete AI-Powered Application

Oru production-ready AI app build pannunga (45-60 mins):

Setup: Choose AI API (OpenAI/Gemini), get API key, setup environment
Backend: Express/Node server setup panni /chat endpoint create panni
Prompts: System prompt + user prompt engineering implement panni
Features: Streaming, error handling, rate limiting, caching implement panni
Frontend: Simple HTML/JS interface build panni
Testing: Different scenarios test panni edge cases handle panni
Cost: API cost tracking implement panni log panni

Tools: Node.js, Express, OpenAI/Gemini API, Postman, Git

Deliverable: Working app + GitHub repo + cost analysis 🚀

Interview Questions

Q1: AI API-powered app design panna, key architectural decisions enna?

A: API choice, cost model, response caching strategy, streaming vs polling, error handling, rate limiting, monitoring. Each decision app performance and cost affect pannum significantly.

Q2: AI API cost high aaguthu – major cost drivers enna?

A: API calls frequency, tokens used per call (input + output), model size, API pricing tier. Main cost drivers manage panna caching, smaller models use, batch processing, background jobs use.

Q3: Streaming vs polling – which approach better? When?

A: Streaming better for interactive apps (chatbots, real-time suggestions), better UX, lower perceived latency. Polling simpler to implement but more network overhead. Streaming preferred modern apps la.

Q4: Error handling AI APIs la critical – common patterns?

A: Retry logic with exponential backoff, fallback responses, error logging, user-friendly error messages, circuit breaker pattern, timeout handling. AI APIs unpredictable, so robust error handling essential.

Q5: Production AI app deploy panna monitoring what important?

A: API latency, token usage, error rates, cost tracking, user feedback, model quality degradation. Monitor panru patterns identify panni quick fixes implement panni.

🎯 Next Steps – Start Building AI Apps Today!

AI + APIs = Superpower for developers! 🦸

Your action plan:

📅 Today: Get an API key (OpenAI or Gemini free tier)
📅 This week: Build a simple chatbot
📅 This month: Add caching, streaming, error handling
📅 Next month: Ship a real AI-powered feature!

Remember: Best AI app = Simple idea + Great execution + Proper engineering 🏆

Start small, ship fast, iterate! The AI API ecosystem is your playground – go build something amazing! 🚀🎉

🧠Knowledge Check

Quiz 1 of 2

AI API keys enga store pannanum?

0 of 2 answered

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Building apps using AI + APIs

🚀 Introduction – AI-Powered Apps Era

🗺️ AI API Landscape – Know Your Options

💡 API Key Security – CRITICAL!

💻 Your First AI API Call – Step by Step

🏗️ AI App Architecture Pattern

🔧 Prompt Engineering for APIs

🎬 Real Project: AI Content Summarizer

💰 Cost Management – Don't Go Broke!

🔄 Streaming Responses – Better UX

🛡️ Error Handling & Resilience

⚠️ Common Mistakes Building AI Apps

🚀 Deployment & Scaling

✅ Key Takeaways

🏁 Mini Challenge

Interview Questions

🎯 Next Steps – Start Building AI Apps Today!