Pipeline Conversations is a fortnightly podcast bringing you interviews and discussion with industry leaders, top technology professionals and others. We discus...
The Evaluation Playbook: Making LLMs Production-Ready 🧪📈
A comprehensive exploration of real-world lessons in LLM evaluation and quality assurance, examining how industry leaders tackle the challenges of assessing language models in production.
Through diverse case studies, we cover the transition from traditional ML evaluation, establishing clear metrics, combining automated and human evaluation strategies, and implementing continuous improvement cycles to ensure reliable LLM applications at scale.
Please read the full blog post here (https://www.zenml.io/blog/the-evaluation-playbook-making-llms-production-ready) and the associated LLMOps database entries here (https://zenml.io/llmops-database).
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Prompt Engineering & Management in Production: Practical Lessons from the LLMOps Database
Prompt engineering is the art and science of crafting instructions that unlock the potential of large language models (LLMs). It's a critical skill for anyone working with LLMs, whether you're building cutting-edge applications or conducting fundamental research. But what does effective prompt engineering look like in practice, and how can we systematically improve our prompts over time?
To answer these questions, we've distilled key insights and techniques from a collection of LLMOps case studies spanning diverse industries and applications. From designing robust prompts to iterative refinement, optimization strategies to management infrastructure, these battle-tested lessons provide a roadmap for prompt engineering mastery.
Please read the full blog post here (https://www.zenml.io/blog/prompt-engineering-management-in-production-practical-lessons-from-the-llmops-database) and the associated LLMOps database entries here (https://zenml.io/llmops-database).
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29:34
LLM Agents in Production: Architectures, Challenges, and Best Practices
An in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices. Drawing from real-world case studies, this article examines the current state of AI agent deployment, infrastructure requirements, and critical considerations for organizations looking to implement these systems safely and effectively.
Please read the full blog post here (https://www.zenml.io/blog/llm-agents-in-production-architectures-challenges-and-best-practices) and the associated LLMOps database entries here (https://zenml.io/llmops-database).
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32:37
Building Advanced Search, Retrieval, and Recommendation Systems with LLMs
Discover how embeddings power modern search and recommendation systems with LLMs, using case studies from the LLMOps Database. From RAG systems to personalized recommendations, learn key strategies and best practices for building intelligent applications that truly understand user intent and deliver relevant results.
Please read the full blog post here (https://www.zenml.io/blog/building-advanced-search-retrieval-and-recommendation-systems-with-llms) and the associated LLMOps database entries here (https://zenml.io/llmops-database).
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Building LLM Applications that Know What They're Talking About 🔓🧠
Explore real-world applications of Retrieval Augmented Generation (RAG) through case studies from leading companies. Learn how RAG enhances LLM applications with external knowledge sources, examining implementation strategies, challenges, and best practices for building more accurate and informed AI systems.
Please read the full blog post here (www.zenml.io/blog/building-llm-applications-that-know-what-theyre-talking-about) and the associated LLMOps database entries here (https://zenml.io/llmops-database).
Pipeline Conversations is a fortnightly podcast bringing you interviews and discussion with industry leaders, top technology professionals and others. We discuss the latest developments in machine learning, deep learning, artificial intelligence, with a particular focus on MLOps, or how trained models are used in production.
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