LLM interview questions and answers

1. 50 LLM interview questions and answers 1. What is a Large Language Model (LLM)? How do LLMs work internally? A Large Language Model is a deep learning model trained on massive text data to understand and generate human-like language. It learns patterns, context, and relationships between words. LLMs use transformer architectures that rely on self-attention mechanisms. This allows them to process context across long sequences efficiently. 2. What is tokenization in LLMs? What is the transformer architecture? Tokenization converts raw text into smaller units called tokens. These tokens are used as numerical inputs for model training and inference. The transformer is a neural network architecture that uses self-attention instead of recurrence. It enables parallel processing and better scalability. 3. What is self-attention? What is pretraining in LLMs? Self-attention allows the model to weigh the importance of different words in a sentence. It helps capture long-range dependencies in text. Pretraining involves training the model on large, general-purpose datasets. It helps the model learn grammar, facts, and general language patterns. 4. What is prompt compression in LLMs? Prompt compression is the technique of reducing prompt size while retaining essential context. It helps optimize token usage and reduce inference cost. 5. What is speculative decoding? Speculative decoding improves inference speed by using a smaller model to predict tokens ahead of time. The larger model then verifies or corrects them. 6. What is long-context handling in LLMs? Long-context handling allows LLMs to process very large documents or

2. conversations. Techniques like attention optimization and memory mechanisms enable this. 7. What is fine-tuning in LLMs? Fine-tuning adapts a pretrained model to a specific task or domain. It improves performance using smaller, task-specific datasets. 8. What is prompt engineering? Prompt engineering is the practice of designing effective input prompts to guide model outputs. It helps achieve accurate and relevant responses. 9. What are embeddings in LLMs? Embeddings are numerical representations of text that capture semantic meaning. They are used for similarity search and retrieval tasks. 10. What is the context window in an LLM? The context window is the maximum number of tokens the model can process at once. Larger context windows allow better understanding of long inputs. 11. What is temperature in text generation? Temperature controls randomness in model outputs. Lower values produce deterministic responses, while higher values increase creativity. 12. What is top-k sampling? Top-k sampling limits the model’s choices to the top k most probable tokens. It helps control output diversity. 13. What is top-p (nucleus) sampling? Top-p sampling selects tokens based on cumulative probability rather than a fixed number. It balances coherence and creativity. 14. What is hallucination in LLMs? Hallucination occurs when the model generates incorrect or fabricated information. It often happens due to lack of reliable context.

3. 15. How can hallucinations be reduced? Hallucinations can be reduced using Retrieval-Augmented Generation, better prompts, and model fine-tuning with verified data. 16. What is Retrieval-Augmented Generation (RAG)? RAG combines LLMs with external knowledge retrieval systems. It improves factual accuracy by grounding responses in real data. 17. What is a vector database in LLM systems? A vector database stores embeddings for fast similarity search. It is commonly used in RAG-based applications. 18. What is zero-shot learning? Zero-shot learning enables LLMs to perform tasks without task-specific training. It relies entirely on pretrained knowledge. 19. What is few-shot learning? Few-shot learning uses a small number of examples in the prompt. It helps guide the model’s behavior effectively. 20. What is instruction tuning? Instruction tuning trains LLMs to follow human-written instructions. It improves usability and alignment with user intent. 21. What is RLHF in LLMs? Reinforcement Learning with Human Feedback aligns model outputs with human preferences. It improves safety and response quality. 22. What is alignment in LLMs? Alignment ensures that model behavior matches human values and intentions. It is critical for responsible AI usage. 23. What is inference in LLMs? Inference is the process of generating outputs from a trained model. It happens when the model is used in real applications.

4. 24. What is latency in LLM applications? Latency refers to the time taken to generate a response. Optimizing latency is important for user experience. 25. What is quantization in LLMs? Quantization reduces model precision to improve speed and reduce memory usage. It helps deploy large models efficiently. 26. What is model distillation? Model distillation transfers knowledge from a large model to a smaller one. It reduces cost while maintaining performance. 27. What is fine-grained evaluation of LLMs? Fine-grained evaluation measures aspects like coherence, factual accuracy, and bias. It goes beyond simple accuracy metrics. 28. What is multimodal LLM? A multimodal LLM processes multiple data types such as text, images, and audio. It enables richer interactions. 29. What is tool calling in LLMs? Tool calling allows LLMs to interact with external APIs or systems. It enables real-world task execution. 30. What is an agentic LLM? An agentic LLM can plan, reason, and take actions autonomously. It uses memory, tools, and feedback loops. 31. What is context leakage in LLMs? Context leakage happens when sensitive information appears in outputs. Proper access control and prompt design help prevent it. 32. What is prompt injection? Prompt injection is an attack that manipulates model behavior through crafted inputs. It poses security risks in LLM systems.

5. 33. How do you secure LLM-based applications? Security is ensured using input validation, role-based access, and monitoring. Guardrails are also commonly applied. 34. What is explainability in LLMs? Explainability refers to understanding why a model generates a response. It improves trust and debugging. 35. What is bias in LLMs? Bias arises when models reflect unfair patterns in training data. It must be identified and mitigated responsibly. 36. What datasets are used to train LLMs? LLMs are trained on large text corpora from books, websites, and code. Data quality is critical for performance. 37. What is catastrophic forgetting? Catastrophic forgetting occurs when fine-tuning causes loss of previous knowledge. Techniques like regularization help prevent it. 38. What is memory in LLM applications? Memory allows LLMs to retain conversation context across interactions. It improves personalization and continuity. 39. What is semantic search? Semantic search retrieves results based on meaning rather than keywords. It relies on embeddings. 40. What is prompt chaining? Prompt chaining breaks complex tasks into multiple steps. It improves reasoning and output quality. 41. What is a system prompt? A system prompt defines the model’s role and behavior. It provides high-level instructions.

6. 42. What is a user prompt? A user prompt is the input provided by the end user. It triggers the model’s response. 43. What is a completion in LLMs? A completion is the text generated by the model in response to a prompt. It represents the final output. 44. What is grounding in LLMs? Grounding ensures responses are based on verified sources. It reduces hallucinations and improves reliability. 45. What are guardrails in LLM systems? Guardrails enforce safety and policy constraints. They prevent harmful or undesired outputs. 46. What are common LLM use cases? Common use cases include chatbots, summarization, code generation, and knowledge assistants. They are used across industries. 47. What challenges exist in deploying LLMs? Challenges include high cost, latency, bias, and governance. Scaling responsibly is also complex. 48. What skills are needed to work with LLMs? Key skills include NLP, deep learning, prompt engineering, and system design. Cloud and MLOps knowledge is also important. 49. How do LLMs differ from traditional NLP models? LLMs are pretrained on massive data and can perform multiple tasks. Traditional NLP models are task-specific. 50. Why are LLMs important in modern AI systems? LLMs enable natural language interaction and automation at scale. They are foundational to Generative AI solutions. To develop the skills of Gen AI, join Hands-on Generative AI training in Chennai.