LLM Cascade Workflow: Local Models vs Flagships

Stop burning your API budget on simple RAG queries that a 7B model could handle. I've been experimenting with a "cascade" architecture where the request hits a cheap local model first, and only escalates to a hosted flagship if the local one fails a validation check.

The goal is to optimize the AI workflow by treating the LLM as a tiered system rather than a single endpoint. I ran a sweep across twenty different local models to see which ones could actually act as a reliable first line of defense.

The Cascade Logic

The setup works like a loop:
1. Tier 1 (Local): A small, fast model attempts to answer the RAG query using the retrieved context.
2. Validation: A lightweight check (or a smaller judge model) verifies if the answer is grounded in the context and isn't a hallucination.
3. Tier 2 (Flagship): If the validation fails or the confidence score is too low, the prompt is routed to a heavy-hitter like Claude 3.5 Sonnet or GPT-4o.

Performance Observations

• Cost Efficiency: The cost drop is massive because ~60-70% of standard enterprise queries are usually simple lookups that don't need 100B+ parameters.
• Latency: Local models respond in milliseconds, but the "loop" adds overhead if the validation step is slow.
• Accuracy: The real-world risk is "false positives" in validation—where the local model hallucinates something that looks correct, and the system never triggers the flagship escalation.

For anyone building a production-grade LLM agent, this is way more sustainable than just piping everything to a paid API. It's basically a routing layer based on complexity.