Many AI products are more dependent on their system architecture than on the specific models they use, such as GPT-4. When relying solely on frontier models, issues like poor retrieval-augmented generation (RAG) designs, inefficient prompts, and hidden assumptions can arise. These problems become evident when using local models, which do not obscure architectural flaws. By addressing these system issues, open-source models can become more predictable, cost-effective, and offer greater control over data and performance. While frontier models excel in zero-shot reasoning, proper infrastructure can narrow the gap for real-world deployments. This matters because optimizing system architecture can lead to more efficient, cost-effective AI solutions that don't rely solely on cutting-edge models.
