Generative AI

Build the working math needed for model training: vectors, matrices, derivatives, probability distributions, information theory, and numerical optimization. Short coding exercises connect each idea to tensors and loss functions.

Frame prediction problems, prepare training and validation sets, choose loss functions, and measure generalization. Train simple supervised and unsupervised models before moving to neural generation.

Build multilayer neural networks with activations, backpropagation, gradient descent, regularization, and normalization. Diagnose underfitting, overfitting, unstable gradients, and training failures.

Trace generative AI from probabilistic models and n-grams through neural networks, GANs, transformers, diffusion models, and foundation models. Connect major research shifts to changes in data, computing, products, and public policy.

Model how observed data could have been produced using joint, conditional, and latent-variable distributions. Compare likelihood-based, implicit, autoregressive, and energy-based approaches.

Collect, clean, label, filter, deduplicate, split, and document text, image, audio, and multimodal datasets. Address data quality, representativeness, consent, licensing, contamination, and leakage.

Convert text and other inputs into model-ready units using character, word, subword, byte, and multimodal tokenization. Inspect vocabulary tradeoffs, sequence length, multilingual behavior, and tokenization failures.

Create useful latent spaces with embeddings, autoencoding objectives, contrastive learning, and dimensionality reduction. Visualize representations and measure whether semantic relationships are preserved.

Factor complex distributions into sequential predictions and generate samples one step at a time. Implement sampling controls such as temperature, top-k, top-p, repetition penalties, and constrained decoding.

Use recurrent neural networks, LSTMs, GRUs, and encoder-decoder architectures for sequence generation. Apply teacher forcing, beam search, and attention while examining the limits of recurrent models.

Build variational autoencoders with probabilistic encoders, decoders, latent sampling, and the evidence lower bound. Use latent interpolation and conditional generation while diagnosing posterior collapse.

Train generators and discriminators through adversarial learning. Work with conditional GANs, convolutional GANs, stabilization techniques, mode collapse, and image-quality assessment.

Construct invertible transformations that support exact likelihood calculation and efficient sampling. Compare coupling layers, autoregressive flows, and continuous normalizing flows with other generative families.

Derive self-attention, positional information, multi-head attention, masking, and transformer blocks. Implement a small transformer and inspect how information moves through it.

Train causal and masked language models with next-token and denoising objectives. Cover pretraining corpora, context windows, decoding, perplexity, and emergent few-shot behavior.

Study how model quality changes with parameter count, data, computing, and training strategy. Compare general-purpose foundation models, domain models, open-weight models, and hosted proprietary models.

Use sparse expert routing to increase model capacity without activating every parameter for every token. Examine load balancing, routing stability, communication costs, and mixture-of-experts serving.

Work with deep learning frameworks, model hubs, hosted APIs, local runtimes, experiment trackers, vector stores, and GPU environments. Compare managed models with open-weight deployment using cost, control, privacy, and maintenance criteria.

Turn pretrained language models into useful assistants through instruction datasets, supervised fine-tuning, conversation templates, and response formatting. Analyze how instruction quality changes model behavior.

Design prompts with clear tasks, context, examples, constraints, and output contracts. Apply context engineering, prompt templates, decomposition, caching, and systematic prompt testing instead of relying on trial and error.

Generate schema-valid JSON, call external functions, and constrain decoding to permitted formats. Validate arguments, handle tool errors, and prevent models from performing unauthorized actions.

Build semantic search with embedding models, similarity measures, indexes, metadata filters, and hybrid lexical-vector retrieval. Evaluate recall, latency, and relevance while managing embedding updates.

Create retrieval-augmented generation pipelines with document parsing, chunking, indexing, query rewriting, reranking, context assembly, and citations. Measure retrieval and answer quality separately to locate failures.

Represent entities and relationships as structured knowledge, then combine graph traversal with language generation. Build a graph-based retrieval pipeline for questions that require multi-hop or connected evidence.

Handle large documents and ongoing conversations using long-context models, summarization, retrieval, compression, and external memory. Test position bias, forgotten constraints, stale memory, and context-window costs.

Use decomposition, verification, search, sampling, and inference-time computation for difficult tasks. Compare reasoning-oriented models and evaluate reasoning without treating fluent explanations as proof of correctness.

Build agents that plan, call tools, observe results, revise actions, and stop safely. Cover state machines, workflow graphs, agent protocols, permissions, human approval, multi-agent patterns, and failure recovery.

Adapt a pretrained model to a task or domain with curated examples, training schedules, validation, and checkpoint selection. Diagnose catastrophic forgetting, overfitting, formatting drift, and weak dataset coverage.

Adapt large models with LoRA, adapters, prompt tuning, and quantized low-rank training. Choose target layers and ranks, merge adapters, and compare quality, memory use, and deployment constraints.

Align model behavior with human or model-generated preferences using reward models, RLHF, PPO, DPO, and related objectives. Address preference-data quality, reward hacking, sycophancy, and alignment tradeoffs.

Generate training examples, labels, critiques, and simulations when real data is scarce or sensitive. Filter synthetic data, prevent model collapse, measure diversity, and preserve a trustworthy human-authored evaluation set.

Transfer behavior from large teacher models into smaller models through logit, feature, and sequence-level distillation. Combine distillation with pruning and quantization to balance quality, speed, cost, and hardware limits.

Model data corruption and learn to reverse it through denoising, score matching, and stochastic sampling. Implement a small diffusion model and compare DDPM, DDIM, classifier-free guidance, and latent diffusion.

Replace convolutional denoisers with transformer-based diffusion architectures and connect diffusion to flow-matching objectives. Compare training stability, sampling speed, scaling behavior, and use in modern image and video systems.

Build text-to-image systems with text encoders, latent representations, denoisers, schedulers, and guidance. Evaluate prompt adherence, composition, visual quality, diversity, and common anatomical or textual failures.

Control and revise generated images through inpainting, outpainting, masks, reference images, structural conditions, adapters, and personalized concepts. Preserve identity and layout while limiting unwanted changes.

Connect text, images, documents, and video frames in shared model architectures. Build visual question-answering and document-understanding tasks while addressing modality alignment and visual hallucination.

Represent and generate speech with acoustic features, neural codecs, text-to-speech models, and speech-to-speech systems. Assess intelligibility, speaker similarity, latency, consent, and voice-cloning risks.

Generate sound effects and music using waveform, spectrogram, token, and diffusion-based approaches. Work with conditioning, musical structure, looping, stem generation, and audio-quality evaluation.

Generate and edit video while maintaining motion, identity, scene structure, and temporal consistency. Cover text-to-video, image-to-video, conditioning, frame interpolation, evaluation, and high computational costs.

Use language models for code completion, transformation, explanation, testing, debugging, and repository-scale assistance. Ground output in project context, execute it in sandboxes, review dependencies, and verify behavior with tests.

Measure quality with task-specific benchmarks, human review, pairwise comparison, model-based judges, and modality-specific metrics. Design evaluation sets that resist contamination and reveal regressions across quality, cost, latency, and safety.

Detect unsupported claims, misplaced confidence, fabricated citations, and unreliable reasoning. Apply grounding, abstention, calibration, verification tools, and human escalation according to the cost of an error.

Design interfaces that help people give context, inspect sources, edit outputs, recover from errors, and retain meaningful control. Address disclosure, accessibility, feedback, consent, automation bias, and high-stakes handoffs.

Use GPUs and other accelerators efficiently through batching, mixed precision, memory management, checkpointing, and optimized kernels. Scale training with data, tensor, pipeline, and expert parallelism while diagnosing communication bottlenecks.

Follow model development from problem definition and data governance through architecture choice, pretraining, evaluation, adaptation, release, and post-release monitoring. Produce data documentation, experiment records, model cards, and release gates.

Take a generative AI application from user need to deployed service: define success criteria, select a model, prototype prompts or retrieval, evaluate risks, build the interface, test, release, monitor, and iterate. The workflow includes fallbacks, human review, cost controls, and incident planning.

Serve models through APIs, batch jobs, streaming responses, and real-time endpoints. Apply quantization, caching, continuous batching, speculative decoding, autoscaling, routing, and capacity planning.

Run generative systems with versioned prompts, models, indexes, datasets, and evaluation suites. Trace requests, monitor quality and drift, collect feedback, control costs, perform staged rollouts, and respond to incidents.

Run compact generative models on phones, browsers, vehicles, and edge devices. Use compression, hardware-aware runtimes, local retrieval, and privacy-preserving designs while working within memory, power, and latency limits.

Defend systems against prompt injection, jailbreaks, data poisoning, malicious documents, tool abuse, secret leakage, model theft, denial of service, and compromised dependencies. Apply threat modeling, isolation, least privilege, input boundaries, and security testing.

Reduce harmful, deceptive, biased, or dangerous outputs through policy design, safety training, classifiers, guardrails, red teaming, and staged release. Measure both over-refusal and under-refusal, with escalation paths for high-risk use.

Handle personal data, confidential prompts, training rights, copyright, likeness, and generated-content ownership. Use minimization, retention controls, provenance metadata, watermarking, content credentials, and documented licensing decisions.

Apply risk frameworks, impact assessments, audit trails, model documentation, access controls, and human oversight. Map systems to relevant laws, sector rules, organizational accountability, and changing requirements such as the EU AI Act.

Compare roles in research, data, model engineering, application development, evaluation, safety, governance, product, and infrastructure. Build proof-of-skill projects, select useful credentials where required, contribute to open work, read research critically, and maintain a reliable process for staying current.