A hands-on workshop on August 29 will teach production-ready retrieval-augmented generation (RAG) using open models entirely, with no API calls required.
Led by Ben Auffarth, the workshop covers hybrid retrieval methods (vector plus keyword search), reranking to improve result relevance, quality measurement with RAGAS, guardrails design, and benchmarking of cost and performance for open-model deployments.
The focus on measured evaluation and open-source tools makes it relevant for teams building AI applications cost-effectively.
What happened
A hands-on workshop on August 29, led by Ben Auffarth (AI Consultant and Founder of Chelsea AI Ventures), teaches production retrieval-augmented generation (RAG) using entirely open models with no API calls. The workshop covers hybrid retrieval combining vector and keyword search, reranking to improve chunk relevance, evaluation using RAGAS, guardrails design, and cost and performance benchmarking for open-model deployments.
Why it matters
RAG systems that combine multiple retrieval methods (vector search plus keyword matching) and rerank results tend to catch relevant information that single-method approaches miss. The workshop emphasizes measurement of quality changes via RAGAS rather than assumption, and benchmarks actual cost and performance on open models—details often skipped in introductory RAG guides. For teams building AI applications on a budget, this addresses a gap: how to evaluate and deploy RAG properly without relying on closed API services.
What to watch
The workshop runs August 29; registration is available via Eventbrite at the provided link.
Ask the AI about this article →
This workshop addresses a practical gap in RAG (retrieval-augmented generation) training: most introductory materials focus on simple vector-based retrieval, but production systems often require hybrid approaches to achieve reliable results. The body emphasizes that vector search alone misses relevant information, which is why the workshop combines it with keyword retrieval and adds a reranking step. The inclusion of RAGAS evaluation and cost-and-performance benchmarking reflects a focus on measurable outcomes rather than assumptions—a distinction that matters for teams deploying to production. The constraint of using entirely open models (no API calls) also sets this workshop apart from many commercial RAG courses, making it relevant for cost-conscious and privacy-conscious teams.
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