NCA-GENL - Prep Course - GenAI & LLMs Associate

Discover why companies invest in generative AI and large language models to cut time and costs, boost decision making, and drive innovation, advancing your career.

Learn how to train a model through a coffee-shop analogy, mapping data to ingredients, a recipe to the algorithm, GPUs to hardware, and inference to production predictions.

Review foundational AI concepts, including artificial intelligence, machine learning, deep learning, and neural networks, and explain models, algorithms, and inference on unseen data.

Explore how decoding predicts the next word using attention and learned patterns, converts it to a vector, and generates output for sentences and paragraphs.

Explore how the attention mechanism powers transformers and self-attention, using group chat analogies to show how positional encoding and highlighted context enable accurate text generation.

Learn how transformer architecture underpins modern language models and multi-stage training, then compare auto regressive, bidirectional, denoising, and sequence-to-sequence prediction strategies.

Learn to test and compare AI models using cross validation, side-by-side output comparisons, AB testing, and pilots, with fold-based evaluation to gauge generalization.

Compare explore and exploit in a/b testing by randomly assigning users to old and new models, balancing risk and learning while controlling for external factors to support data-driven optimization.

Select the best model for your use case by balancing performance, cost, ease of use, and reliability. Consider affordability, deployability, risk, and documentation, then re-evaluate choices over time with testing.

Compare models by selecting SLM versus LM, assess accuracy with rouge metrics and cosine similarity, and use cross-validation and a/b testing to optimize GenAI performance.

Learn prompt engineering to customize model output, using zero-shot, one-shot, and few-shot prompting, plus chain-of-thought reasoning, with practical real-world examples.

Explore retrieval augmented generation (rag) and how to augment llm outputs with a vector database and knowledge base to provide up-to-date, sourced responses and reduce hallucination.

Collect data from public data sets, enterprise data, and external APIs to support model development. Ensure ethical, privacy-aware handling with compliant storage and access controls.