LoRA (Low-Rank Adaptation) in AI
A new section for Fine-Tuning Techniques is created to hold the LoRA document, and the LLM Architecture section is de-duplicated.
Ais
Function Calling Support in LLM Models
Explains function calling (tool use) in LLMs: how models emit structured requests to invoke external functions, the request-execute-return loop, provider support, and practical reliability notes.
What is Speculative Decoding?
Explains speculative decoding, which pairs a small draft model with a large target model to accelerate LLM inference without changing outputs.
Mixture of Experts in AI
Explains sparse Mixture-of-Experts (MoE) architecture with conditional computation, router/gate mechanisms, load balancing, and trade-offs vs. dense models.
Autoregressive Image Generation
Explains autoregressive image generation as sequential visual-token prediction using Transformer-style next-token modeling.
What is a Diffusion Model?
Explains diffusion models as generative AI systems that learn to create data by reversing a noising process.
RLVR vs. the Agent Loop: Training-Time vs. Inference-Time
Distinguishes RLVR as training-time weight updates from inference-time agent verification loops.
The Modern LLM Training Pipeline
Explains the four-stage modern LLM training pipeline from pre-training through verifiable-reward RL.
Where RL Fits: Training vs. Inference in the LLM Pipeline
Explains that RL in LLMs is a training/alignment stage, not inference, with pipeline context.
Reinforcement Learning (ELI-Teen Explainer)
Teen-friendly explainer of reinforcement learning agents, rewards, exploration, delayed rewards, and applications.
Coding LLM Training with SFT and Verifiable RL
Explains scripted coding-LLM training with teacher traces, synthetic bugs, tests, SFT, and verifiable RL.
Prefix Caching in AI
Explains prefix caching for reusing attention KV computations to speed up shared-prefix AI inference.
World Models in AI
Explains AI world models as internal predictive representations for planning across RL, LLMs, and robotics.
Jailbreaking LLMs: A Security Researcher's Field Guide
Field guide to LLM jailbreaking attack surfaces, threat modeling, defenses, and responsible disclosure.
Turing Test
Defines the Turing test as a text-only behavioral test of machine intelligence through human-like conversation.
AdapTime: Adaptive Temporal Reasoning in LLMs
Paper summary of AdapTime, an adaptive planner for temporal reasoning in LLMs.
Quantization-Aware Training (QAT) in AI
Explains QAT for training neural networks to retain accuracy under low-precision quantization.
Model Type Classification by Modality (Multimodal, Vision, Image Generation)
Classifies multimodal, vision, and image-generation models by their input/output modalities.
AI Harness Engineering Principles
Principles for designing AI harnesses: context, tools, verification, autonomy, observability, and composition.
Anti-Narration in Harness Engineering
Harness pattern that forces verification before accepting fluent AI outputs as correct.