Edition 27 – RAG Evaluation
The Drift is a collection of top content we've published recently at Arize AI. This month's edition features a great workflow for troubleshooting RAG applications, a RAG roadmap that highlights the technical aspects, deep dives into the latest research, and industry-specific checklists for LLM observability. As always we conclude with a list of some of our favorite news, papers, and community threads.
Read on and dive in...
Troubleshoot LLMs and RAG with Retrieval and Response Metrics
Retrieval augmented generation has been shown to be highly effective for complex query answering, knowledge-intensive tasks, and enhancing the precision and relevance of responses for AI models, especially in situations where standalone training data may fall short.
However, you only benefit from RAG if you're continuously monitoring your LLM system at common failure points. Here's a great workflow for troubleshooting RAG applications from Amber R. . Read It.
The Needle In a Haystack Test: Evaluating the Performance of LLM RAG Systems
Retrieval-augmented generation (RAG) underpins many of the LLM applications in the real world today, from companies generating headlines to solo developers solving problems for small businesses. With RAG’s importance likely to grow, ensuring its effectiveness is paramount. Evaluating the performance of? RAG systems. The evaluation of RAG, therefore, has become a critical part in the development and deployment of these systems.
Aparna Dhinakaran dives into one innovative approach to this challenge (with co-author Evan Jolley )--the “Needle in a Haystack” test, first outlined by Gregory Kamradt . Read It.
The LLM Retrieval Augmented Generation (RAG) Roadmap
This RAG roadmap lays out a clear path through the complex processes that underpin RAG from data retrieval to response generation. Amber R. explores these steps in detail and examine the differences between online and offline modes of RAG. The journey through the RAG roadmap will not only highlight the technical aspects but also demonstrate the most effective ways to evaluate your search and retrieval results. Read it.
Phi-2 Model
领英推荐
With only 2.7 billion parameters, Phi-2 surpasses the performance of Mistral and Llama-2 models at 7B and 13B parameters on various aggregated benchmarks. Notably, it achieves better performance compared to 25x larger Llama-2-70B model on multi-step reasoning tasks, i.e., coding and math. Furthermore, Phi-2 matches or outperforms the recently-announced Google Gemini Nano 2, despite being smaller in size. Sally-Ann DeLucia and Aman Khan dive into Phi-2 and some of the major differences and use cases for a small language model (SLM) versus an LLM. Read It.
RAG vs Fine-Tuning
Sally-Ann DeLucia and Amber R. discuss “RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture.” This paper that explores a pipeline for Fine-tuning and RAG, and presents the tradeoffs of both for multiple popular LLMs, including Llama 2-13B, GPT-3.5, and GPT-4. Read it
The Definitive LLM Observability Checklist for Media & Entertainment
From new special effects techniques to tools to power a streamlined customer experience, the media and entertainment industry is being transformed by generative AI. As early-adopters see outsized gains, many are finding that having robust LLM evaluation and LLM observability in place is critical to their success.
Informed by experience working with top media companies with successful LLM apps deployed in the real world, this checklist covers essential elements to consider when assessing an LLM observability provider. Read it.
The Definitive LLM Observability Checklist for Healthcare, Life Sciences & Consumer Health
Given the potential harms and regulatory risks intrinsic to applying AI in healthcare, having robust LLM evaluation and LLM observability is critical. How can teams deploy generative AI reliably and responsibly – and what should they look for when assessing partners? Informed by experience working with top researchers and providers that have successful LLM apps deployed in the real world, this checklist covers essential elements to consider when assessing an LLM observability provider. Read it.
Staff Picks ??
Here's a roundup of our team's favorite news, papers, and community threads recently:?