RAG
How to Build a Better RAG Pipeline: Complete Guide
LLMs don't know your data. RAG bridges that gap. Master ingestion, extraction, chunking, embedding, and real-time sync.
The challenge
LLMs don't know your enterprise data—internal docs, customer conversations, CRM records, technical specs, compliance documents. Without access to this context, even advanced AI becomes just another search engine.
RAG pipeline steps
- Ingestion: Identify knowledge sources (wikis, SaaS tools like Slack, Jira, HubSpot)
- Extraction: Convert complex PDFs, tables, images into useful text
- Chunking & Embedding: Split text into semantic segments, convert to vectors
- Persistence: Store vectors in optimized database
- Refreshing: Keep data synchronized with source systems in real-time
Production considerations
- Reliability & error handling (retries, exponential backoffs)
- Security & compliance (access controls, encryption, audit trails)
- Performance & scale (ingestion speed, query response times, costs)
Needle's approach
Direct integrations with enterprise tools. Intelligent extraction handling complex documents. Real-time synchronization across all systems. Enterprise security built-in.
Building RAG pipelines from scratch is complex. Start with Needle and focus on use cases that drive business value. Read the complete guide.
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