The Hallucination Problem
of LLM responses contain factual errors when asked about current events
average knowledge cutoff for major language models
accuracy improvement with real-time web grounding
RAG Use Cases
From chatbots to knowledge bases—ground any LLM application in real-time web data.
RAG Pipelines
Augment your retrieval system with live web data alongside your vector database
Chatbot Context
Let your chatbots browse the web on behalf of users to answer current questions
Knowledge Refresh
Keep your knowledge base current by automatically pulling fresh web content
Fact Verification
Cross-reference LLM outputs against authoritative web sources
How It Works
Simple architecture. Powerful results.
User Query
User asks a question requiring current information
"What is the current price of Bitcoin?"Web Retrieval
Tryb fetches live data from relevant sources
Reads CoinGecko, Binance, Coinbase in parallelContext Injection
Clean, structured data is added to the LLM prompt
{"btc_price": 67432.50, "24h_change": "+2.3%", ...}Grounded Response
LLM generates accurate, cited response
"Bitcoin is currently trading at $67,432.50..."Integration Example
Combine Tryb with the Vercel AI SDK for grounded responses.
import { TrybClient } from '@tryb/sdk'
import { openai } from '@ai-sdk/openai'
import { generateText } from 'ai'
const tryb = new TrybClient({ apiKey: 'sk_...' })
async function answerWithWebContext(question: string) {
// 1. Determine what to search
const searchQuery = await generateText({
model: openai('gpt-4o-mini'),
prompt: `Generate a search-friendly version of: "${question}"`
})
// 2. Fetch live web data
const webData = await tryb.read({
url: `https://www.google.com/search?q=${encodeURIComponent(searchQuery.text)}`,
format: 'markdown'
})
// 3. Generate grounded response
const response = await generateText({
model: openai('gpt-4o'),
prompt: `Answer this question using ONLY the provided context.
Question: ${question}
Web Context:
${webData.content}
Provide a factual answer with source attribution.`
})
return response.text
}Always Current
No more stale embeddings or outdated vector stores. Every query gets live web data.
Source Attribution
Every piece of information comes with its source URL for verification and citation.
Low Latency
Average 2-second response time. Fast enough for real-time chat applications.