AI features are no longer nice-to-have—they're competitive necessities. As Rishikesh Baidya, our CTO, notes: "The question isn't whether to add AI—it's how to add it responsibly without breaking your product." At Softechinfra, we've integrated AI features into products like TalkDrill and ExamReady.
Identifying AI Opportunities
AI Integration Patterns
Pattern 1: LLM API Integration
The simplest pattern—call an external AI service:
import OpenAI from 'openai'const openai = new OpenAI()
async function generateSummary(text: string) {
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages: [
{
role: 'system',
content: 'Summarize the following text in 3 bullet points.'
},
{ role: 'user', content: text }
],
max_tokens: 200
})
return response.choices[0].message.content
}
Pattern 2: RAG (Retrieval-Augmented Generation)
Combine your data with LLM capabilities:
Pattern 3: AI Agents
LLMs that can take actions:
// Agent loop with tool use
async function runAgent(userRequest: string) {
const tools = [searchDatabase, sendEmail, createTask] while (true) {
const response = await llm.generate({
prompt: userRequest,
tools: tools.map(t => t.schema)
})
if (response.type === 'tool_call') {
const result = await executeTool(response.tool, response.args)
userRequest += \nTool result: ${result}
} else {
return response.content
}
}
}
Production Implementation
Prompt Engineering
const SYSTEM_PROMPT = You are a helpful assistant that analyzes customer feedback.RULES:
- 1. Identify sentiment (positive/negative/neutral)
- 2. Extract key topics mentioned
- 3. Suggest actionable improvements
OUTPUT FORMAT: JSON with fields:
- sentiment: string
- topics: string[]
- suggestions: string[]
Keep suggestions actionable and specific.
Error Handling & Fallbacks
async function safeAICall(
fn: () => Promise,
fallback: T
): Promise {
try {
const result = await fn()
if (!isValidOutput(result)) {
logger.warn('Invalid AI output, using fallback')
return fallback
}
return result
} catch (error) {
logger.error('AI call failed', error)
return fallback
}
} Streaming for Better UX
async function* streamResponse(prompt: string) {
const stream = await openai.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: prompt }],
stream: true
}) for await (const chunk of stream) {
yield chunk.choices[0]?.delta?.content || ''
}
}
Caching Strategy
Reduce costs by 90% with smart caching:
async function cachedAICall(prompt: string) {
const cacheKey = createHash('sha256').update(prompt).digest('hex') const cached = await redis.get(cacheKey)
if (cached) return JSON.parse(cached)
const result = await aiService.generate(prompt)
await redis.setex(cacheKey, 3600, JSON.stringify(result)) // 1 hour TTL
return result
}
Quality & Safety
Testing AI Features
- Unit tests for prompt templates
- Evaluation datasets with expected outputs
- A/B tests for prompt variations
- Edge case testing (empty input, long input, adversarial)
- Output validation before use
Guardrails
| Guardrail | Purpose | Implementation |
|---|---|---|
| Input Validation | Prevent injection attacks | Sanitize before sending to LLM |
| Output Filtering | Block harmful content | Content moderation API |
| Rate Limiting | Control costs and abuse | Per-user quotas |
| Human Review | High-stakes decisions | Approval workflow |
See our testing AI applications guide for comprehensive testing strategies.
Cost Management
User Experience Principles
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