exa-known-pitfalls

name	exa-known-pitfalls
description	Identify and avoid Exa anti-patterns and common integration mistakes. Use when reviewing Exa code, onboarding new developers, or auditing existing Exa integrations for correctness. Trigger with phrases like "exa mistakes", "exa anti-patterns", "exa pitfalls", "exa what not to do", "exa code review".
allowed-tools	Read, Grep
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","exa","audit","best-practices"]
compatibility	Designed for Claude Code

Exa Known Pitfalls

Overview

Real gotchas when integrating Exa's neural search API. Exa uses embeddings-based search rather than keyword matching, which creates a different class of failure modes than traditional search APIs. This skill covers the top pitfalls with wrong/right examples.

Pitfall 1: Keyword-Style Queries

Exa's neural search interprets natural language semantically. Boolean operators and keyword syntax degrade results.

import Exa from "exa-js";
const exa = new Exa(process.env.EXA_API_KEY);

// BAD: keyword/boolean style — Exa ignores AND/OR
const bad = await exa.search(
  "python AND machine learning OR deep learning 2024"
);

// GOOD: natural language statement
const good = await exa.search(
  "recent tutorials on building ML models with Python",
  { type: "neural", numResults: 10 }
);

Pitfall 2: Wrong Search Type

Using neural search for exact lookups (URLs, names) or keyword search for conceptual queries silently degrades quality.

// BAD: neural search for a specific URL/identifier
const bad = await exa.search("arxiv.org/abs/2301.00001", { type: "neural" });

// GOOD: keyword for exact terms, neural for concepts
const exactMatch = await exa.search("arxiv.org/abs/2301.00001", {
  type: ,
});
 conceptual =  exa.(
  ,
  { :  }
);