Industrial AI – LLM Test

Deep dive into the mechanics of transformer models: multi-head attention, scaled dot-product attention, self-/cross-attention, encoder–decoder pipelines, positional encoding (absolute, relative, RoPE, ALiBi), residual pathways, feed-forward layers, layer norms, masking techniques, parallelism strategies, and architecture-specific innovations (GPT, BERT, T5, LLaMA, Mistral).

Evaluates expertise in LLM training pipelines: supervised fine-tuning, instruction tuning, alignment techniques (RLHF, RLAIF, DPO), small-parameter methods (LoRA, QLoRA, Adapters), distributed training (FSDP, DeepSpeed, Tensor Parallelism), model compression, quantization (int8, int4), distillation, dataset curation, curriculum learning, and training stability optimization for large-scale models.

Covers task-level LLM application design: classification, NER, QA, summarization (extractive/abstractive), translation, text generation, reasoning tasks, semantic search, information extraction, knowledge-grounded dialogue, long-context comprehension, multi-document reasoning, and advanced prompt+retrieval workflows for domain-specific tasks.

Explores advanced prompt strategies: zero-/few-shot prompting, chain-of-thought, self-consistency, tool-use prompting, structured prompting, safety-aware prompting, instruction hierarchy design, context management for long prompts, hallucination mitigation, and optimization of model outputs for accuracy, consistency, and adherence to industrial constraints.

Deep assessment of knowledge-grounded LLM systems: embedding models, vector indexing (FAISS, Weaviate, Pinecone), chunking strategies, hybrid search (BM25 + dense embeddings), retriever–reader pipelines, multi-hop retrieval, contextual relevance scoring, memory augmentation, enterprise knowledge integration, and optimization for factuality and retrieval precision at scale.

Covers rigorous LLM evaluation across accuracy, coherence, reasoning, and factuality: perplexity, BLEU/ROUGE, BERTScore, GPTScore; model interpretability tools (attention visualization, activation probing); safety evaluation (toxicity, bias, harmful content detection); hallucination diagnostics; governance frameworks; compliance considerations (GDPR, HIPAA), and ethical deployment principles for industrial AI systems.

Focuses on productionizing LLMs: scalable inference (vLLM, TensorRT-LLM), API serving, model versioning, CI/CD pipelines for LLM workflows, monitoring (latency, drift, hallucinations), autoscaling, secure deployment patterns, rate limiting, caching, cost-performance optimization, cloud-native orchestration (Kubernetes), and integration with enterprise stacks (ERP, CRM, IoT systems).

Explores next-generation LLM capabilities: vision-language systems (CLIP, LLaVA), speech-to-text and audio-language models (Whisper, AudioLM), multimodal embeddings, cross-lingual alignment, multilingual LLM training, task transfers across languages, domain-specific LLM specialization (legal, medical, manufacturing), and integration of structured/graph data with LLMs.

Evaluates high-level innovation capabilities: design of new LLM architectures (Mixture-of-Experts, sparse transformers), scaling-law–guided research, multi-agent LLM collaboration frameworks, cognitive architectures, memory-augmented LLMs, data synthesis pipelines, reinforcement-driven LLM behavior shaping, patentable AI innovations, and architecting enterprise-wide LLM ecosystems integrating research, safety, and production constraints.