After debugging 50+ AI integrations, I've seen the same problems over and over. The good news? 95% of AI issues fall into 5 categories, and all are fixable.
Problem #1: AI Returns Incorrect or Inconsistent Answers
Symptom: Your AI gives wrong answers, contradicts itself, or returns different responses to the same question.
Root Causes & Solutions
Cause 1: Temperature Set Too High
Temperature controls randomness. Higher = more creative but inconsistent. Lower = deterministic and consistent.
✓ SOLUTION:
// For factual tasks (customer support, data extraction)
temperature: 0.1 // Very consistent
// For creative tasks (content writing, brainstorming)
temperature: 0.7-0.9 // More variedCause 2: Vague or Contradictory Prompts
If your prompt is ambiguous, the AI will guess what you mean—often incorrectly.
❌ BAD:
"Summarize this"Too vague. How long? What format? What to focus on?
✅ GOOD:
"Summarize this document in 3 bullet points (max 50 words each). Focus on: 1) Main findings, 2) Recommendations, 3) Next steps. Use business-friendly language."Cause 3: Model Hallucination
The AI is literally making up information that sounds plausible but is wrong.
✓ SOLUTIONS:
- Add explicit instructions: "Only use information provided in the context. If you don't know, say 'I don't know.'"
- Use RAG (Retrieval-Augmented Generation): Inject factual data into prompts
- Lower temperature: 0.1-0.3 reduces hallucinations
- Add validation: Check AI responses against source data
- Try Claude 2: Hallucinates less than GPT models in our testing
Cause 4: Insufficient Context
The model doesn't have enough information to answer correctly.
✓ SOLUTION:
// Provide rich context in system message
{
role: "system",
content: `You are a customer support AI for AcmeCorp.
Company info:
- We sell SaaS project management software
- Pricing: $29/mo (Starter), $99/mo (Pro), $299/mo (Enterprise)
- Free trial: 14 days, no credit card required
- Support hours: Mon-Fri 9am-6pm EST
When answering:
1. Be friendly and professional
2. Always mention our 14-day free trial
3. If question is about billing, escalate to billing@acmecorp.com
4. Never make promises about features we don't have`
}Problem #2: Slow API Responses (Taking 10-30+ seconds)
Symptom: Users are waiting too long for AI responses. Timeouts occurring.
Solution 1: Implement Streaming
Instead of waiting for the complete response, stream it word-by-word like ChatGPT does.
// OpenAI streaming example
const stream = await openai.chat.completions.create({
model: "gpt-3.5-turbo",
messages: messages,
stream: true // Enable streaming
});
for await (const chunk of stream) {
const content = chunk.choices[0]?.delta?.content || '';
// Send each chunk to the user immediately
console.log(content);
}Solution 2: Use Faster Models
| Model | Avg Response Time | Quality |
|---|---|---|
| GPT-4 Turbo | 8-15 seconds | ★★★★★ |
| GPT-3.5 Turbo | 1-3 seconds ⚡ | ★★★★☆ |
| Claude Instant | 2-4 seconds | ★★★★☆ |
💡 Use GPT-3.5 for 80% of requests, GPT-4 only when you need the best quality.
Solution 3: Reduce max_tokens
Shorter responses = faster generation.
// Instead of letting AI write essays
max_tokens: 2000 // ❌ Slow
// Limit to what you actually need
max_tokens: 300 // ✅ 3-5x fasterProblem #3: API Costs Are Too High
Symptom: Your monthly OpenAI bill is $1,000+ and growing. Need to reduce costs.
Real Example: $15K → $4K/month (73% Savings)
1. Implement Caching (40% savings)
Cache identical requests for 1-24 hours using Redis. Saved $6,000/mo.
// Simple caching example
async function getCachedResponse(cacheKey, prompt) {
// Check cache first
const cached = await redis.get(cacheKey);
if (cached) return JSON.parse(cached);
// Call AI if not cached
const response = await callOpenAI(prompt);
// Store in cache for 1 hour
await redis.setex(cacheKey, 3600, JSON.stringify(response));
return response;
}2. Use GPT-3.5 for Simple Tasks (23% savings)
GPT-4 costs 20x more. Use it only when necessary. Saved $3,500/mo.
3. Optimize Prompts (10% savings)
Shorter prompts = lower costs. Remove unnecessary examples. Saved $1,500/mo.
Problem #4: Rate Limit Errors (429 Too Many Requests)
What It Means
You're hitting your API rate limit (requests per minute). OpenAI's default limits are surprisingly low:
- • Free tier: 3 requests/minute
- • Pay-as-you-go: 3,500 requests/minute
- • Enterprise: Custom limits
Solutions
- Implement Exponential Backoff:
async function callWithRetry(fn, maxRetries = 3) { for (let i = 0; i < maxRetries; i++) { try { return await fn(); } catch (error) { if (error.status === 429 && i < maxRetries - 1) { const delay = Math.pow(2, i) * 1000; // 1s, 2s, 4s await new Promise(resolve => setTimeout(resolve, delay)); } else { throw error; } } } } - Request Higher Limits: Go to platform.openai.com → Settings → Limits. Usually approved in 48 hours.
- Queue Requests: Don't send 100 requests in parallel. Use a queue to control rate.
Quick Fixes: FAQ
Why does my AI give different answers to the same question?
This is caused by the 'temperature' parameter. Temperature controls randomness. Set temperature to 0 for consistent responses (deterministic), or 0.1-0.3 for slightly varied but consistent answers. Default is usually 0.7-1.0 which causes variation.
How do I stop AI from making up facts (hallucinating)?
1) Lower temperature to 0.1-0.3, 2) Explicitly instruct: 'Only use information provided. If you don't know, say I don't know.', 3) Use RAG (Retrieval-Augmented Generation) to inject factual context, 4) Implement fact-checking validation, 5) Consider Claude 2 which hallucinates less than GPT models.
What does 'rate limit exceeded' error mean?
You've hit your API rate limit (requests per minute/day). Solutions: 1) Implement exponential backoff retry logic, 2) Queue requests instead of sending all at once, 3) Request higher limits from your provider (usually approved within 48 hours), 4) Upgrade your API tier for higher limits.
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