Ennia Suijkerbuijk - Evaluating LLM Frameworks

RAG (Retrieval Augmented Generation) frameworks are model-agnostic, allowing flexibility to plug in different LLMs while maintaining the same information retrieval system

Setting up objective evaluation metrics is crucial - recommended to use 12+ different metrics including faithfulness scores, semantic similarity, and response quality measurements

Ground truth establishment is essential for proper evaluation - requires significant human input and careful dataset curation with more than 60 examples

Automation of evaluation is key - manual review becomes impractical at scale, requiring frameworks that can automatically assess model outputs

Hallucinations remain a major risk with LLMs - RAG helps mitigate this by grounding responses in verified knowledge sources

Model selection should be based on multiple factors including latency, cost, and quality metrics rather than just accuracy

Client data and documentation should be properly embedded and chunked for effective retrieval

Regular testing and monitoring of the framework is necessary as new models emerge weekly

Prompt engineering remains critically important - different models require different prompting approaches

Human feedback loops and oversight should be maintained even with automated evaluation systems in place