Easy everything in LLMs & Generative AI, page 2
Explain GAN architecture, generator and discriminator roles, and objective function
Tests adversarial training as a minimax game. Strong answers: generator maps noise z to fakes; discriminator classifies real versus fake; both optimize V(D,G)=E[log D(x)]+E[log(1-D(G(z)))].
Walk me through building a weather agent with get_weather
Register get_weather, let the model emit parameters, execute it yourself, feed the result back, then synthesize the answer.
How does function calling work in modern LLMs?
Schemas in the prompt; model emits JSON name and arguments; client executes and returns results.
Describe a basic RAG architecture and its two main components
This tests retrieval-generation separation. Good answers name the retriever, which fetches relevant documents, and the generator, which synthesizes an answer using those documents plus the query.
Describe supervised fine-tuning for a pre-trained language model
Tests if you know SFT aligns a base model to instructions using curated prompt-completion data. A strong answer covers next-token prediction on completions, conversational formats, and small learning rates.
Explain Chain-of-Thought prompting, its reasoning mechanism, and ideal use cases
This tests reasoning scaffolding. A good answer says CoT makes the model emit intermediate steps before the final answer, excelling at multi-step math and logic versus direct instructions.
How would you construct zero-shot and few-shot prompts for feedback classification?
Tests knowledge of zero-shot versus few-shot prompt structure. Zero-shot gives instructions, labels, and format without examples; few-shot prepends 2-4 labeled demonstrations before the target input. Red flag: calling an example-containing prompt zero-shot.
Explain word embeddings and why they beat one-hot encoding for large vocabularies
Embeddings cluster similar meanings in low-dimensional space, while one-hot vectors are orthogonal, huge, and semantically blank.
Google TPU: Built for Matrix Math
A TPU is a specialist ASIC, not a faster GPU; it trades graphics flexibility for matrix-math throughput per watt. Google deploys them for TensorFlow, JAX, and PyTorch at scale. They excel at CNNs but can lag on tasks needing rasterization or recurrent logic.
The OpenAI API: Access to Foundational AI Models
The OpenAI API provides access to influential models like GPT and DALL-E. It enables developers to integrate generative AI into commercial applications and research, building on technology that catalyzed the current AI boom.
Hugging Face Hub: The GitHub for Machine Learning
Think of the Hugging Face Hub as the GitHub for machine learning. It's a central platform to find, share, and collaborate on millions of models, datasets, and demo apps. Use it to download a pre-trained model or share your own.
AI Governance: Rules for Building Intelligent Systems
AI governance creates rules of the road for intelligent systems, ensuring they're safe, fair, and transparent. It applies when governments pass laws or companies form ethics boards. The footgun is treating this as only a legal problem, not a technical one.
Deepfakes: AI-Generated Media Impersonations
Deepfakes are AI-generated media that convincingly impersonate people. Think of it as digital puppetry, where an AI manipulates a face or voice. They're used for film effects and satire, but also for misinformation. The footgun: assuming you can spot one.
Data Bias: When AI Inherits Our Flaws
Generative AI learns patterns from its training data. Data bias occurs when this data contains skewed perspectives or stereotypes, which the model then reproduces and amplifies. This is why an image generator might default to stereotypes.
KV Cache: Don't Recompute, Just Remember
KV Cache speeds up LLM text generation by storing intermediate calculations (Key/Value vectors) instead of recomputing them for every new token. It's a standard optimization in inference engines.
Model Cards: The 'Nutrition Label' for AI Models
A model card is the nutrition label for an AI model, summarizing its ingredients, intended use, and risks. Found in model repos, it details training data, performance, and ethical guardrails.
The AI Alignment Problem
AI alignment is about making sure an AI pursues our intended goals, not just the literal instructions. It's critical for autonomous systems in medicine or finance. The footgun is assuming a clear objective prevents unintended, harmful outcomes.
Human Evaluation: Judging AI When Metrics Aren't Enough
Human evaluation is the ultimate reality check for AI, using people to judge qualities like fluency and coherence that automated scores can't capture. It's essential for tasks like summarization but is too slow and costly to use for everything.
Speech-to-Text (ASR): Turning Spoken Words into Data
Speech-to-Text (ASR) is a digital stenographer, turning spoken language into machine-readable text. It's the engine behind voice assistants, automated call routing, and video captioning.
Multimodal Models: Beyond Just Text
A multimodal model understands the world by connecting different data types, like images and text, instead of just one. It's how AI generates images from descriptions or answers questions about a photo. The footgun is assuming more data types always helps.
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