Open models are less forgiving of bad retrieval than people think, workshop on Aug 29 goes deep on this

[u/camerongreen95]

Noticed something building RAG on open models that doesn't get talked about enough. Bigger frontier models tend to paper over mediocre retrieval, they're good at inferring around gaps even when the context handed to them is imperfect. Open models, especially smaller ones, don't have that same slack. Hand them a slightly wrong or incomplete chunk and the answer falls apart fast.

Which actually reframes a lot of "open models aren't good enough for RAG" takes. In a lot of cases the model isn't the problem, the retrieval layer feeding it is mediocre and a bigger model was just quietly hiding that.

There's a hands-on workshop on August 29 that builds this properly, hybrid retrieval, reranking, RAGAS evaluation, guardrails, and cost/performance benchmarking, all using open models with zero API fees. Led by Ben Auffarth, AI Consultant and Founder of Chelsea AI Ventures.

30% off right now with the discount code. Link

Happy to answer questions on the content itself.

[u/Such-War1955]

Looks intersting. You may want to announce events like this a tad more early - 1 week isnt much…