Exa

@Exa find 10 recent articles similar to https://example.com/ai-safety-paper and return summaries for each

Found 10 semantically similar pages published in the last 90 days. Results include: 'Alignment Research Overview' (arxiv.org, published 12 days ago, summary: discusses current approaches to AI alignment including RLHF and constitutional AI methods), 'Safety Considerations in Large Models' (openai.com/research, 8 days ago, summary: examines failure modes in production systems), and 8 additional articles spanning academic preprints, industry blogs, and research lab publications. All results ranked by embedding similarity to the seed URL.

This example shows Exa's semantic search capability — it finds content by meaning, not keywords. Requires an API key with search quota. The similarity threshold and recency filters are configurable. Results quality depends on the seed URL's specificity; vague starting points yield broader matches.

@Exa create a webset called 'Competitor Product Pages' seeded with search for 'SaaS project management tools pricing' and set up a weekly monitor

Created webset 'Competitor Product Pages' (ID: ws_abc123) with initial search query returning 47 pages. Configured weekly monitor (ID: mon_xyz789) to refresh results every Monday at 9 AM UTC. The webset includes automatic enrichment for page titles, descriptions, and publication dates. You can now query this webset directly or wait for the first automated update in 6 days.

This demonstrates Exa's data collection and monitoring workflow — useful for competitive intelligence or content tracking. The webset persists in your Exa account and accumulates data over time. Be aware: monitors consume API quota on each run, and webset storage has account-tier limits.

@Exa generate an answer to 'What are the main criticisms of React Server Components?' with citations from the last 6 months

Main criticisms of React Server Components from recent discussions: (1) Increased complexity in mental model — developers must track client/server boundaries explicitly [source: vercel.com/blog/rsc-patterns, Jan 2024], (2) Limited ecosystem compatibility with existing libraries expecting client-side rendering [source: github.com/reactjs/rfcs/discussions, Dec 2023], (3) Debugging challenges when errors span server and client execution contexts [source: kentcdodds.com/blog/rsc-gotchas, Feb 2024]. Proponents counter that these trade-offs enable better performance and data fetching patterns.

This showcases Exa's answer generation with inline citations — it synthesizes a response from multiple sources rather than returning raw search results. The AI combines Exa's retrieval with its own reasoning. Accuracy depends on source quality and recency filters; always verify citations for high-stakes decisions.

When Exa beats manual research for feature prioritization

A 6-person product team ships every two weeks and needs to track what competitors are launching. The PM runs a Monday monitor that searches for new product announcements, changelog pages, and pricing updates across 12 competitor domains. Exa's semantic similarity search catches feature launches even when the competitor doesn't use your exact keywords—critical when everyone calls the same capability by different names. The citation-backed answer generation turns 40 minutes of tab-hopping into a 3-minute Slack summary. This works until you need to track more than 20 sources or want sub-hourly updates; at that scale, the monitor runs get expensive and you're better off with a dedicated scraping setup. If your roadmap review happens weekly and you're comparing against fewer than 15 competitors, Exa's monitor-to-answer workflow is the fastest path from 'what shipped' to 'what we do next'.

How Exa accelerates first-draft documentation for support teams

A 3-person support team at a B2B SaaS company needs to build internal docs for a new integration their eng team just shipped. They paste the integration's landing page URL into Exa's find-similar tool, pull 8-10 semantically related setup guides from other tools in the same category, then use generate-an-answer to synthesize a first-draft walkthrough with citations. The whole loop takes 15 minutes instead of the usual 2-hour research-and-write cycle. Exa's strength here is cold-start speed—you don't need an existing corpus or fine-tuned embeddings. The trade-off: if your product has deep technical nuance or non-standard terminology, the generated answers lean generic and you'll rewrite 60% of it anyway. For straightforward integrations and common workflows, Exa turns 'we have no docs' into 'we have a reviewable draft' in one support-team work session.

When Exa's webset import beats manual LinkedIn stalking

A 4-person sales team reviews 12-15 inbound leads per week and needs to know if each prospect's company is a real fit before the first call. They create a webset, import a CSV of company domains, and run enrichment to pull recent funding news, tech stack signals, and hiring pages. Exa's get-contents tool returns structured text from each company's about page and careers page in one API call—no Puppeteer, no rate-limit dance. The AE preps the call deck in 10 minutes instead of 40. This breaks down if you're doing high-volume outbound (hundreds of leads per day) or need CRM-native enrichment; at that point, Clearbit or Apollo's native integrations are faster. For inbound-focused teams closing 10-20 deals per quarter, Exa's webset workflow is the right speed-to-structure trade-off without paying for an enterprise data vendor.