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2,099 resources · Page 47 of 88
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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.
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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LLMs 28. Learn Prompting
This guide covers LLM prompt engineering techniques and practical prompting skills, with topics such as framing problems, representative practice…
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LLMs 29. LoRA Series
This guide covers LoRA and its variants, parameter-efficient fine-tuning (PEFT) techniques for adapting large language models, practical…
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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…
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LLMs 31. LLMs: Inference
This guide covers LLM inference topics and skills including inference system design, performance optimization (latency and throughput), serving…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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LLMs 39. Reinforcement Learning Applications in Natural Language Processing
This technical interview prep framework covers reinforcement learning applications in natural language processing, including policy optimization…
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LLMs 40. Large Language Models (LLMs) — Training Section
This learning-oriented guide covers LLM training data, formats, selection, and scaling across supervised fine-tuning (SFT), reward modeling (RM)…
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LLMs 41. Large Language Models (LLMs) — Methods for Generating SFT Data
This guide explains practical methods for constructing supervised fine-tuning (SFT) datasets for large language models, covering human annotation…
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LLMs 42. LLMs: Hardware, Memory, and Performance
This practical guide examines LLM hardware, memory, and performance trade-offs, covering parameterization versus system constraints, memory…