High DemandHands-on LabRAG
Enterprise RAG on SharePoint/Confluence
Retrieval-Augmented Generation (RAG) allows you to connect your LLMs to your internal data (SharePoint, Confluence, document bases). This workshop teaches you to build a high-performance, evaluated, and production-ready RAG system.
Duration
3h30
Audience
Developers, Data Engineers, ML Engineers
Technologies
LangChain, Vector DBs (Pinecone/Weaviate/ChromaDB), OpenAI/Anthropic
Format
On-site or Remote
Detailed program
0:00 – 0:20
Introduction: Retrieval Strategies
- What is RAG and why is it essential
- Retrieval strategies: semantic search, keyword search, hybrid
- Re-ranking: improving relevance with Cohere, Anthropic
- Chunking strategies: how to split your documents
0:20 – 1:20
Lab 1: Index and query your data
- Connect a data source (SharePoint, Confluence, local files)
- Split and index documents in a vector DB
- Implement semantic search
- Create a QA chain with LangChain
- Add citations (source attribution)
1:30 – 2:15
Lab 2: Evaluation and Optimization
- Create an evaluation dataset (reference Q&A)
- Measure precision (retrieval accuracy, answer quality)
- Measure latency and costs
- A/B testing of different chunking strategies
- Optimize with re-ranking and metadata filters
2:25 – 3:30
Hardening: Security and Operations
- ACL mirroring: respecting SharePoint/Confluence permissions
- Cache: reduce costs with intelligent caching
- Cost optimization: choose the right embedding model, vector DB
- Monitoring: tracking quality over time
- Incremental index update
Deliverables
- Ready-to-use RAG starter (code + config)
- Evaluation dataset with metrics
- Operational notes: costs, scaling, maintenance
- Security checklist: ACL, confidentiality, GDPR
Prerequisites
- Python (intermediate level)
- Access to SharePoint or Confluence (to test with your data)
- OpenAI or Anthropic account (API key)
Pricing
€990 excl. tax / person
€4,500 excl. tax / group (up to 8 participants)
Level
Intermediate
Accessibility
Workshop accessible to people with disabilities. Contact our disability referent: accessibilite@ikasia.ai
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