Prepare for your next interview.

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1. Articles

   LLM Latency Interview Questions: TTFT, Inter-Token Delay, and Tail Latency

   Practice LLM latency interview questions on TTFT, token gaps, tail latency, percentiles, load testing, and tracing with worked request-timing examples.

2. Articles

   LLM Observability System Design Interview: Traces, Quality Metrics, Costs, and Failure Analysis

   Practice LLM observability system design interview on architecture, debugging, evaluation, safety, and trade-offs with a focused 2026 guide and seven-day plan.

3. Articles

   LLM Post-Training Interview Questions: SFT, DPO, PPO, and GRPO Trade-Offs

   Prepare for LLM post-training interviews with SFT, DPO, PPO, and GRPO trade-offs, checked numerical examples, reward failures, and evaluation choices.

4. Articles

   LLM Tokenization Interview Questions: BPE, Special Tokens, and Cost Trade-Offs

   Prepare for LLM tokenization interviews with BPE traces, special-token pitfalls, multilingual examples, and practical token budget and cost calculations.

5. Articles

   LLM Tool-Calling Interview Questions: Parallel Calls, State, and Error Recovery

   Practice LLM tool-calling interview questions with parallel-call examples, state tracking, idempotency, partial failures, and safe recovery after timeouts.

6. Articles

   LLMs 20. Large Models (LLMs) External Knowledge Base Optimization — Hard Negative Sampling

   This learning hub covers hard negative sampling for retrieval in external knowledge base optimization with large models, detailing why hard negatives…

7. Articles

   LLMs 21. RAG (Retrieval-Augmented Generation) — Evaluation

   This practical learning guide covers evaluation of retrieval-augmented generation (RAG) systems, explaining why RAG evaluation differs from standard…

8. Articles

   LLMs 22. Retrieval-Augmented Generation (RAG) Optimization Strategies

   This systems-level guide covers RAG architecture, interactions between document processing, retrieval, reasoning, and generation, common performance…

9. Articles

   LLMs 23. Large Models (LLMs) + RAG — Key Challenges and Corresponding Solutions

   This playbook-style guide covers retrieval-augmented generation (RAG) and large language model engineering, detailing 12 common failure points—such…

10. Articles

    LLMs 24. Large Language Models (LLMs) RAG Optimization Strategies — RAG-Fusion Edition

    This resource covers RAG optimization strategies with a focus on RAG-Fusion, including multi-perspective retrieval, fusion techniques, handling vague…

11. Articles

    LLMs 25. A Knowledge-Graph-Based LLM Retrieval-Augmentation Implementation Strategy

    This guide covers knowledge-graph-based retrieval augmentation for large language models, explaining how Graph RAG differs from vector RAG, methods…

12. Articles

    LLMs 26. Large Language Models (LLMs): Parameter-Efficient Fine-Tuning (PEFT)

    This article explains parameter-efficient fine-tuning (PEFT) for large language models, covering how PEFT differs from full-model retraining…

13. Articles

    LLMs 27. Adapter Fine-Tuning (Adapter-Tuning)

    This guide covers adapter fine-tuning for large language models, explaining why adapters emerged, how adapter-tuning works, parameter-efficient…

14. Articles

    LLMs 28. Learn Prompting

    This guide covers LLM prompt engineering techniques and practical prompting skills, with topics such as framing problems, representative practice…

15. Articles

    LLMs 29. LoRA Series

    This guide covers LoRA and its variants, parameter-efficient fine-tuning (PEFT) techniques for adapting large language models, practical…

16. Articles

    LLMs 30. How to Use LoRA in the PEFT Library

    This deep-dive guide explains LoRA theory and practical usage in the PEFT library, covering why LoRA works, PEFT's internal implementation…

17. Articles

    LLMs 31. LLMs: Inference

    This guide covers LLM inference topics and skills including inference system design, performance optimization (latency and throughput), serving…

18. Articles

    LLMs 32. Large Language Models (LLMs): Reinforcement Learning — PPO Section

    This guide explains Proximal Policy Optimization (PPO) within Reinforcement Learning from Human Feedback (RLHF) for large language models, emphasizing…

19. Articles

    LLMs 33. Continued Pretraining (Pretrain): Sample Concatenation Strategies

    This technical guide analyzes sample concatenation strategies in continued pretraining for large language models, explaining why concatenation is…

20. Articles

    LLMs 34. LoRA-Based Continued Pretraining for LLaMA 2

    This conceptual guide covers LoRA-based continued pretraining for LLaMA 2, including the motivation, the specific problem it addresses, training…

21. Articles

    LLMs 35. Large Language Models (LLMs) — Evaluation

    This guide covers methods for evaluating large language models, including limitations of traditional benchmarks, interaction-based and dynamic…

22. Articles

    LLMs 36. Large Language Models (LLMs) — Reinforcement Learning

    This learning-oriented guide explains reinforcement learning concepts as applied to large language models, covering RLHF, the evolution of alignment…

23. Articles

    LLMs 37. Large Language Models (LLMs) — Reinforcement Learning (RL) Section

    This conceptual guide covers reinforcement learning for large language models, specifically RLHF and its variants, the place of RL in the LLM training…

24. Articles

    LLMs 38. Large Language Models (LLMs) Reinforcement Learning — PPO Section

    This guide covers PPO in LLM Reinforcement Learning from Human Feedback (RLHF), detailing policy updates, sampling and feedback mechanics, learning…