Getting Started
Quick Start Guide
Create an AI agent and query it
TL;DR
Create an AI agent and query it. Twig account at app.twig.so Data to index: PDF/Word files, or a documentation website URL
Key Takeaways
- Prerequisites
- Step 1: Create Your Organization
- Step 2: Add a Data Source
- Step 3: Create an Agent
- Step 4: Test Your Agent
- What's Next?
Create an AI agent and query it.
Prerequisites
- Twig account at app.twig.so
- Data to index: PDF/Word files, or a documentation website URL
Step 1: Create Your Organization
On first login:
- Go to app.twig.so
- Sign in with email/password or SSO (Google, Microsoft)
- Fill the organization setup form:
- Organization name (required)
- Industry (optional dropdown)
- Invite team members (optional, can skip)
- Click Complete Setup
Expected result: You see the main dashboard with "Data", "Agents", "Playground" menu items
Step 2: Add a Data Source
Option A: Upload a File
- Click Data in left navigation
- Click Add Data Source button (top right)
- Select File Upload from the modal
- Choose file: PDF, DOCX, TXT (max 50MB per file)
- Click Upload
- Wait for processing (progress bar shows chunking, embedding)
Expected result: Status changes to "Active" with green indicator. Shows chunk count (e.g., "256 chunks indexed").
Processing time: ~1-3 minutes for a 100-page PDF
Option B: Connect a Website
- Click Data in left navigation
- Click Add Data Source button
- Select Website Crawler
- Enter base URL:
https://docs.example.com - Set max pages (default: 100, max: 10,000)
- Click Start Crawl
Expected result: Status "Crawling" → "Processing" → "Active". Shows pages crawled count.
Processing time: ~5-10 minutes for 100 pages
Other Connectors
- Confluence: OAuth flow, select spaces
- Google Drive: OAuth flow, select folders
- Slack: OAuth flow, select channels (last 90 days of messages)
See Data Sources for connector-specific setup
Step 3: Create an Agent
- Click Agents in left navigation
- Click Create Agent button
- Fill the form:
- Name: e.g., "Customer Support Agent" (required)
- Data Sources: Check the data source from Step 2 (required)
- System Prompt (optional): "You are a customer support assistant. Provide concise answers with citations."
- RAG Strategy: Leave as "Redwood" (default)
- Model: Leave as "GPT-4" (default)
- Click Create Agent
Expected result: Agent appears in agents list with status "Active". Agent ID is visible (format: agent_abc123).
Step 4: Test Your Agent
- Click Playground in left navigation
- Select your agent from the dropdown
- Type a question in the input field (must relate to your indexed data)
- Press Enter or click Send
Expected result:
- Response appears in chat window (typical: 2-5 seconds)
- Citations shown below response with document names and chunk IDs
- Retrieved sources panel (right side) shows the chunks used
How to verify it works:
- Click a citation → opens source document at that location
- Check "Sources Used" panel → shows 5-10 chunks with similarity scores (0.0-1.0)
- Response references content from your data (not generic AI knowledge)
What's Next?
Improve Agent Accuracy
- Add data sources: Agents → [Your Agent] → Data Sources tab → Add more
- Tune system prompt: Agents → [Your Agent] → Configuration → edit System Prompt field
- Change RAG strategy: Agents → [Your Agent] → Configuration → RAG Strategy dropdown
- Redwood: 1-2s latency (current)
- Cedar: 2-3s latency, query rewriting
- Cypress: 3-5s latency, reranking, best accuracy
- Test queries: Run 10-20 test questions in Playground, check citation accuracy
Deploy
- REST API: Settings → API Keys → Generate, use
/api/v1/queryendpoint - Embed widget: Deployments → Widget → Copy embed code (iframe)
- Chrome extension: Chrome Web Store → "Twig AI" → Install, paste API key
- Slack integration: Integrations → Slack → Authorize workspace
Monitor
- Inbox: Review → Inbox → see all queries, mark accurate/inaccurate
- Analytics: Dashboard → shows query count, avg latency, accuracy rate
- Evals: Evaluation → Create Test Set → add question/expected answer pairs, run against agent
Troubleshooting
Data Source Stuck at "Processing"
Symptom: Status stays "Processing" for >10 minutes
Diagnostic steps:
- Data → [Your Data Source] → Logs tab → check for error messages
- Common errors:
- "401 Unauthorized" → OAuth token expired, reconnect
- "Rate limit exceeded" → Wait 1 hour, crawler resumes automatically
- "Invalid file format" → Convert to PDF/DOCX, re-upload
Fix: Based on error message. If no error after 30 minutes, contact support with data source ID.
Agent Returns "No relevant information found"
Symptom: Agent responds with "I don't have information about that"
Diagnostic steps:
- Playground → Check "Sources Used" panel → if empty, retrieval failed
- Possible causes:
- Query doesn't match indexed content (semantic mismatch)
- Data source not connected to agent
- Similarity threshold too high (default: 0.7)
Fix:
- Agents → [Your Agent] → Data Sources tab → verify data source is checked
- Try exact phrase from your document in query
- Lower similarity threshold: Configuration → Advanced → Similarity Threshold → 0.5
Query Takes >10 Seconds
Symptom: Response latency exceeds 10 seconds
Diagnostic steps:
- Playground → Query → Check response time breakdown (bottom of response)
- Identify slow step:
- Retrieval >3s → Too many chunks indexed, contact support
- LLM >8s → Model overloaded, retry in 1-2 minutes
- Embedding >1s → Network issue, check connection
Fix: Switch to Redwood strategy (Configuration → RAG Strategy) for 1-2s responses.
When This Doesn't Apply
These steps assume default settings on Free/Pro plan. Enterprise configurations may differ.
Next steps: Authentication for API access, or Core Concepts for RAG terminology.
Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the ask query parameter:
GET /dev/getting-started/quick-start.md?ask=<question>
The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
Related Pages
Last updated January 26, 2026


