🔍
RAG for Business Use Cases: Intermediate Patterns
An intermediate-level technical training for engineers deploying RAG systems to real business problems. Move beyond basic quickstart tutorials and learn the decisions that determine production RAG quality: which chunking strategy fits your document type, how hybrid search beats pure vector retrieval on enterprise data, how cross-encoder reranking improves precision without breaking your latency budget, and which evaluation metrics actually matter for your domain. Three worked examples — support chatbot, legal document review, medical protocol RAG — give you patterns you can apply the next day.
Duration
2 days
Level
Intermediate
Price
9.99 EUR/month (all courses included)
Max group
12 participants
What you will learn
+Choose the right chunking strategy (fixed, recursive, semantic) for each document type
+Build a hybrid retrieval pipeline combining BM25 and vector search
+Add cross-encoder reranking with latency and cost tradeoffs understood
+Apply metadata filtering to eliminate irrelevant documents before embedding comparison
+Define domain-specific evaluation metrics for support, legal, and medical RAG
+Implement safety gates that block unsafe outputs in high-stakes contexts
+Estimate indexing and per-query costs with a reusable template
Course program
Module 1: Chunking Strategies: From Fixed-Size to Semantic
3h00- Fixed-size vs recursive vs semantic chunking: when each wins
- Token budget design and overlap rationale
- Semantic chunking with sentence embeddings and cosine thresholds
- Cost estimation template: indexing cost + per-query cost by domain
Module 2: Hybrid Search and Reranking
3h30- Why pure vector search fails on product codes and exact identifiers
- BM25 + dense vector fusion with EnsembleRetriever
- Metadata filtering as a free precision boost
- Cross-encoder reranking: accuracy vs latency tradeoffs
- Decision matrix: which retrieval stack for which use case
Module 3: Domain-Specific Evaluation and Worked Examples
4h00- Metric selection by domain: support (accuracy), legal (citation precision), medical (safety recall)
- Worked example 1: support chatbot with hybrid search and no-answer rate
- Worked example 2: legal contract review with citation enforcement and reranking
- Worked example 3: medical protocol RAG with safety gates
- Automated evaluation harness with weekly regression alerts
Ready to get started?
9.99 EUR/month — All courses included, cancel anytime