Perplexity known pitfalls

Perplexity Known Pitfalls

Overview

Real gotchas when integrating Perplexity Sonar API. Perplexity uses an OpenAI-compatible chat endpoint but performs live web searches -- a fundamentally different paradigm from standard LLM completions. These pitfalls come from treating it like a regular chatbot.

Prerequisites

• Perplexity API key configured
• Understanding of OpenAI-compatible chat API format

Pitfalls

1. Using It as a Generic Chatbot

Perplexity searches the web per request. Using it for tasks that don't need web search wastes money.

# BAD: general chatbot (wastes a search query)
response = call_perplexity("Write me a haiku about cats")
# Costs $0.005+ for something any LLM can do offline

# GOOD: leverage web search capability
response = call_perplexity(
    "What are the latest Next.js 15 features released this month?",
    search_recency_filter="month"
)

2. Ignoring Citations

Perplexity returns [1], [2] markers in text with a separate citations array. Ignoring them loses the key value prop.

data = response.model_dump()  # or response.json() for raw HTTP
answer = data["choices"][0]["message"]["content"]
citations = data.get("citations", [])  # NOT in choices — top-level field

# BAD: displaying raw markers
print(answer)  # "According to [1], Node.js 22 adds..."

# GOOD: replace markers with links
import re
for i, url in enumerate(citations, 1):
    answer = answer.replace(f"[{i}]", f"{i}")

3. Using Wrong SDK Import

There is no @perplexity/sdk or perplexity Python package. Use the standard OpenAI client.

// BAD — this package doesn't exist
import { PerplexityClient } from "@perplexity/sdk";

// GOOD — use OpenAI client with Perplexity base URL
import OpenAI from "openai";
const client = new OpenAI({
  apiKey: process.env.PERPLEXITY_API_KEY,
  baseURL: "https://api.perplexity.ai",
});

4. Not Setting max_tokens

Without max_tokens, responses can be arbitrarily long, increasing costs unpredictably.

// BAD: no token limit — output cost can spike
await client.chat.completions.create({
  model: "sonar-pro",  // $15/M output tokens!
  messages: [{ role: "user", content: "Tell me about AI" }],
});

// GOOD: always set max_tokens
await client.chat.completions.create({
  model: "sonar-pro",
  messages: [{ role: "user", content: "Tell me about AI" }],
  max_tokens: 1024,
});

5. No Recency Filter for Time-Sensitive Queries

Without search_recency_filter, Perplexity may cite outdated articles.

# BAD: may return articles from any time period
response = call_perplexity("current Bitcoin price")

# GOOD: constrain to recent results
response = call_perplexity(
    "current Bitcoin price",
    search_recency_filter="day"  # hour | day | week | month
)

6. Sending Full Conversation History

Each message in the conversation may trigger new search queries. Sending 20 turns of history is expensive and slow.

# BAD: 20 turns of history = many search queries
messages = long_history + [{"role": "user", "content": "summarize"}]

# GOOD: summarize context, send focused query
messages = [
    {"role": "system", "content": "Answer based on web search."},
    {"role": "user", "content": f"Context: {summary}\nQuestion: {question}"}
]

7. Using sonar-pro for Simple Queries

sonar-pro costs 3-15x more than sonar. Using it for simple factual lookups wastes budget.

// BAD: sonar-pro for a trivial question
await client.chat.completions.create({
  model: "sonar-pro",  // $3 input + $15 output per M tokens
  messages: [{ role: "user", content: "What is the capital of France?" }],
});

// GOOD: match model to complexity
const model = isComplexQuery(query) ? "sonar-pro" : "sonar";

8. Mixing Allowlist and Denylist in Domain Filter

search_domain_filter supports either allowlist (include) or denylist (exclude with - prefix), but not both in the same request.

// BAD: mixing modes
search_domain_filter: ["python.org", "-reddit.com"]  // ERROR

// GOOD: pick one mode
search_domain_filter: ["python.org", "docs.python.org"]  // Allowlist
// OR
search_domain_filter: ["-reddit.com", "-quora.com"]  // Denylist

9. Not Caching Search Results

Every uncached call performs a web search. At scale, duplicate queries burn budget.

// BAD: same query hits API every time
app.get("/search", (req, res) => {
  const result = await client.chat.completions.create({ ... });
  res.json(result);
});

// GOOD: cache by query hash
const cache = new LRUCache({ max: 1000, ttl: 3600_000 });
app.get("/search", (req, res) => {
  const key = hash(req.query.q);
  if (cache.has(key)) return res.json(cache.get(key));
  const result = await client.chat.completions.create({ ... });
  cache.set(key, result);
  res.json(result);
});

10. Wrong Base URL

The API is at api.perplexity.ai, not api.perplexity.com.

// BAD
baseURL: "https://api.perplexity.com"  // Wrong domain

// GOOD
baseURL: "https://api.perplexity.ai"   // Correct

Code Review Checklist

• Uses openai package, not fake @perplexity/sdk
• Base URL is https://api.perplexity.ai
• max_tokens set on every request
• Citations parsed from response.citations array
• search_recency_filter used for time-sensitive queries
• Caching implemented for repeated queries
• Model routing: sonar for simple, sonar-pro for complex
• Conversation history trimmed before sending
• PII sanitized from queries
• Domain filter uses only allowlist OR denylist, not both

Error Handling

Pitfall	Impact	Detection
No caching	3-5x cost overrun	Check cache hit rate metric
Wrong model	Budget waste	Grep for sonar-pro in simple query paths
No max_tokens	Unpredictable costs	Grep for create() calls without max_tokens
PII in queries	Privacy violation	Run sanitization check in CI

Output

• Identified anti-patterns in existing code
• Applied fixes for each pitfall
• Code review checklist for ongoing quality

Resources

• Perplexity API Documentation
• Perplexity Model Guide
• OpenAI Compatibility