Llm

Overview of model drift, detection, mitigation, and LLM-specific issues like knowledge staleness and provider drift.

Concrete web-search example showing how Tool-DC strategic anchor grouping reduces schema-confusion in tool calls.

Overview of AgentFlow, an agent architecture that trains a planner with Flow-GRPO for multi-turn tool use.

Overview of Tool-DC, a try-check-retry framework for robust long-context tool-calling with large tool registries.

Explains top-k retrieval in RAG, tradeoffs for choosing k, reranking patterns, and similarity thresholds.

Overview of SWE-bench and SWE-bench Pro, the real-world GitHub issue fixing benchmarks used to evaluate AI coding ability.

Using a language model to evaluate another model's outputs as a scalable proxy for human preference judgments.

Comprehensive guide to LLM fine-tuning methods including full, parameter-efficient, and preference-based approaches with modern recipes and tools like LoRA and DPO

Definition and best practices for deterministic grading in LLM evaluation using code-based rules instead of model-in-the-loop judgment.

Prompting technique where an AI model is guided — or learns — to reason through a problem step by step before arriving at the final answer, rather than jumping straight to the conclusion.

Explains how AI models maintain context across multiple exchanges using conversation history injection rather than internal memory.