Most mainframe troubleshooting RAGs fail at the same place: retrieval. An abend code like S0C7 is a near-exact lookup, not a fuzzy semantic match — but vector search happily returns the S0C4 chunk because the embeddings sit close together. And job dependencies are graph-shaped: an abend in step 3 cascades to …

Training & Adaptation Strategy

1. What approaches exist for training or adapting an LLM? — Pretraining, fine-tuning, instruction tuning, prompt engineering, RAG.

2. Base model vs instruction-tuned model? — Pure next-token predictor vs one aligned to follow instructions.

3. When would you choose fine-tuning over RAG? — Stable domain knowledge, style/format control, latency sensitivity.

4. When would …

A starter question bank for screening entry-level GenAI engineers. Grouped by theme, covering fundamentals through production concerns.

Fundamentals

1. What is generative AI vs discriminative AI? — Generative models learn to produce new data; discriminative models learn decision boundaries to classify or predict.

2. What is a large language model (LLM)? — A neural …

If you are shipping agentic systems in 2026 and these six terms are fuzzy, you are building on sand. This is the foundational vocabulary — not hype, not vendor marketing, just the concepts that actually show up when you are designing, debugging, or scaling an agent pipeline.

The Six Terms

Model …