Best Llm Finder Pipeline

LLM Finder Pipeline

(Agentic RAG, Multi-Agent Systems, Vision with Reasoning)

In 2025, the most advanced systems no longer rely on a single large, general-purpose model. Instead, they use multiple LLMs of different sizes and specialties, each handling a specific task like a team of experts working together on a complex problem.

However, properly evaluating an LLM within its specific role in such an architecture is very challenging, **since it needs to be tested directly in the context where it is used**.


We are going to build three distinct, production-grade AI pipelines and use them as testbeds to measure how different models perform in their assigned roles. Our evaluation framework is built on three core principles:

1. Role-Specific Testing: We evaluate models based on the specific task they are assigned within the pipeline, whether it’s a fast router, a deep reasoner, or a precise judge.
2. Real-World Scenarios: Instead of stopping at abstract tests, we are going to run our models through complete, end-to-end workflows to see how they perform under realistic conditions.
3. Holistic Measurement: We are going to measure everything that matters, from operational metrics like cost and latency to qualitative scores like faithfulness, relevance, and scientific validity.

Our evaluation strategy for choosing the right LLM for each component is divided into three real-world cases.

• Agentic RAG for Deep Document Analysis
  • Setting up the RAG Environment
  • Preprocessing our Dataset
  • Creating the Two-Pass Router Agent
  • Building the Recursive Navigator
  • Executing the Full Navigation Process