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Learn to build a contextual chatbot using LLM and vector database, exploring embedding, search, and answer generation with Langchain.
Explore the fundamentals of building reliable AI agents, their business applications, and key challenges in agentic automation.
Explore foundation models' applications in data analytics, cleaning, and linkage. Learn solutions for production deployment challenges in the modern data stack.
Explore challenges and solutions for deploying LLMs in production, covering design patterns, vector databases, reliability enhancement, and cost management strategies.
Explore methods to quantify and detect hallucinations in LLMs, understanding their impact on model outcomes and learning efficient techniques to prevent misinformation and biased results.
Explore UI paradigms, design patterns, and challenges of production copilot systems embedded in SaaS applications leveraging LLMs and generative AI models.
Explore techniques for monitoring and measuring drift in unstructured data models, focusing on vector/embedding drift for image and language models in production environments.
Explore LLM-powered meme creation using ImgFlip's dataset. Learn about model selection, fine-tuning, and challenges in productionizing for millions of users. Includes prototype demonstration.
Explore strategies for guiding LLMs in software tasks while implementing crucial safety checks to prevent errors and ensure reliable performance.
Explore building defensible apps with LLMs by leveraging the entire tech stack. Learn strategies to create unique products using "reasoning machines" beyond simple model wrappers.
Explore challenges in deploying LLMs to production, from infrastructure to output quality. Learn potential solutions for model downtime, latency, variability, and prompt injections.
Explore agentic relationship management to navigate complex personal and professional networks in the digital age.
Explore LLM tool usage: importance, types, prompting strategies, and challenges. Gain insights from LangChain's CEO on enhancing Language Model applications.
Explore vector databases and LLMs for real-time context retrieval in AI applications. Learn to enhance generative models with up-to-date external knowledge, preventing hallucinations and improving accuracy.
Explore ML observability for unstructured data with Arize. Learn about embeddings, monitoring challenges, and solutions through expert insights and a product demo.
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