Building LLM Apps with Prompt Engineering NVBLLMAPE-QA

NVBLLMAPE-QA
Cena na vyžiadanie

Large language models are transforming how organisations build products, automate workflows, and unlock value from data. We believe the future belongs to organisations that can learn, master, and apply AI capabilities at pace and scale. This workshop introduces modern prompt engineering techniques as the fastest path to building practical LLM-powered applications.Learners will work with NVIDIA NIM, powered by the open-source Llama 3.1 large language model, alongside the LangChain library to structure and orchestrate LLM workflows. Through hands-on exercises, participants will build generative applications, document analysis pipelines, and chatbot assistants, while establishing the foundations required for more advanced techniques such as retrieval-augmented generation and parameter-efficient fine-tuning.

Explain the core principles of large language models and how prompt engineering influences model behaviour
Apply iterative prompt engineering best practices to improve output quality, reliability, and relevance
Use NVIDIA NIM to access and deploy LLM capabilities for inference-based applications
Design and implement structured LLM workflows using LangChain
Build application code for text generation, large-scale document analysis, and chatbot assistants
Describe how prompt engineering underpins advanced techniques such as retrieval-augmented generation and parameter-efficient fine-tuning
Evaluate LLM outputs and implement strategies to mitigate common risks such as hallucinations and prompt injection

Building LLM Apps with Prompt Engineering

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