Less is More: How Good RAG Design Lets You Use Smaller Language Models

In the pursuit of optimizing AI applications, it is emphasized that smaller, cost-effective language models can perform comparably to larger ones if retrieval-augmented generation (RAG) pipelines and prompt engineering are well-designed. By providing highly relevant context, these smaller models can generate effective responses without relying heavily on their built-in knowledge. Key strategies for efficient RAG design include employing smart retrieval strategies, relevance filtering through reranking, and precise prompt engineering. These involve using query rewriting, optimizing embedding models, setting relevance thresholds, and crafting prompts to ensure clarity and relevance. Real-world testing, such as at Vectorize, has demonstrated that focusing on retrieval quality and relevance filtering leads to good results with smaller models, highlighting the importance of pipeline design over model size. This approach not only enhances cost-effectiveness and reliability but also underscores the significance of delivering the right information to the model at the right time, rather than relying on the sheer size of the model.