Building stateful LLM applications

In the ninth session of _AI Talks_, we discussed evaluating the generation component of Retrieval-Augmented Generation (RAG) pipelines. We began with an introduction to RAG, emphasizing how it retrieves relevant documents before generating responses. The evaluation process is crucial as it ensures the generated output is accurate and relevant. We explored key challenges in generation, such as factual inaccuracies (hallucinations), coherence issues, and the balance between response quality and latency. The importance of evaluating both retrieval and generation components was highlighted, as errors in either stage can negatively impact the final output.

Next, we examined different evaluation methods, focusing on both automated and human-driven approaches. Automated metrics like BLEU, ROUGE, and METEOR help measure relevance, while GPT-4-based scoring or linguistic measures assess fluency and coherence. However, automated methods have limitations, necessitating human evaluation frameworks that assess responses based on relevance, fluency, and informativeness. A/B testing was also discussed as a practical way to compare different generation models in production, measuring real-world performance through user engagement and satisfaction metrics.

Finally, we explored how to automate the evaluation process using tools like OpenAI evals and Hugging Face's evaluation metrics library, enabling continuous monitoring and improvement of RAG pipelines. Case studies demonstrated best practices in chatbot response assessment and content generation workflows, providing valuable insights into practical applications. Ethical considerations were a key discussion point, emphasizing the need for fairness, avoidance of bias, and alignment with organizational values to ensure responsible AI-generated content.

Slides link: https://www.canva.com/design/DAGbHzNHwv8/3qmRsCEQZr5-UILJ1jujOQ/view?utm_content=DAGbHzNHwv8&utm_campaign=designshare&utm_medium=link2&utm_source=uniquelinks&utlId=h3cf98efc5e