
Explore the fundamentals of building reliable AI agents, their business applications, and key challenges in agentic automation.

Learn to build a contextual chatbot using LLM and vector database, exploring embedding, search, and answer generation with Langchain.

Explore Wardley Mapping for strategic prompt engineering in AI, learning to analyze value chains and target efforts effectively.

Explore how linguistics enhances LLM performance and shapes future improvements in AI language models.

Explore recent approaches to building recommender systems using LLMs, focusing on language understanding and generation templates for various recommendation tasks.

Explore code completion with LLMs, covering model selection, infrastructure, evaluation, and cost considerations. Gain insights into building effective AI-assisted coding tools.

Explore AI-powered data systems for unstructured content, addressing challenges and sharing key lessons for end-to-end applications in enterprise environments.

Explore RLHF, efficient training techniques, on-device ML, and optimizations in modern machine learning production with insights from a Hugging Face lead engineer.

Explore LIMA, a LLaMa-based model fine-tuned on 1,000 curated prompts, producing impressive responses without user data or RLHF. Discover insights on language model alignment from this innovative approach.

Explore evolving AI governance frameworks for LLM-driven applications in production, addressing critical changes needed for successful implementation.

Explore best practices for secure, reliable, and scalable open-source LLM deployments in enterprise settings.

Optimize GPU costs and improve uptime by leveraging preemptible instances for LLM services, overcoming challenges and maximizing savings.

Explore MLOps community trends through interactive tools visualizing Slack message content, presented by open-source contributor and LangChainGo maintainer Travis Cline.

Explore QA systems using LLMs and RAGs, learning to create powerful question-answering bots on custom datasets.

Explore key drivers shaping Generative AI's future, including scaling laws, agents, multi-modality, and open-source contributions, while addressing challenges like GPU shortages and regulations.

Explore observability techniques for LLMs, including instrumentation, telemetry analysis, and OpenTelemetry integration. Learn to enhance reliability and gather crucial evaluation data for AI systems.

Explore real-world LLM applications, overcome limitations, and leverage techniques like RAG to ground results. Learn to bridge LLMs with enterprise Gen AI apps using vector-supported databases.

Explore the challenges and best practices in building a Retrieval Augmented Generation system, focusing on data quality, query engines, and contextualization for informed decision-making in your projects.

Explore cutting-edge techniques for red-teaming generative AI and building validation engines to probe models for security, ethics, and safety issues. Learn a framework to manage AI risk across the model lifecycle.

Explore the latest in open-source AI: deployment challenges, evolving licenses, and hardware advancements. Gain insights into project maintenance, permissive architectures, and improving software portability for AI development.

Explore retrieval evaluation's crucial role in LLM applications, focusing on its impact on response quality. Delve into the link between retrieval accuracy and answer quality, emphasizing thorough evaluation methods.