glossary
**AI/ML Glossary**
**Core LLM Concepts**
**A-F**
| Term | Definition |
|------|------------|
| **Alignment** | Training AI to be helpful, harmless, and honest |
| **Attention** | Mechanism for tokens to consider other tokens |
| **BPE** | Byte Pair Encoding, a tokenization algorithm |
| **Chain-of-Thought** | Prompting technique for step-by-step reasoning |
| **Context Window** | Maximum tokens an LLM can process at once |
| **Decoder** | Transformer block that generates output tokens |
| **DPO** | Direct Preference Optimization, simpler RLHF alternative |
| **Embedding** | Dense vector representation of text |
| **Encoder** | Transformer block that processes input |
| **Fine-tuning** | Adapting a pretrained model to new data |
| **Few-shot** | Providing examples in the prompt |
**G-L**
| Term | Definition |
|------|------------|
| **Ground Truth** | Correct labels for training or evaluation |
| **Hallucination** | LLM generating plausible but false information |
| **Inference** | Running a trained model to get predictions |
| **Jailbreak** | Circumventing LLM safety measures |
| **KV Cache** | Stored key-value pairs for efficient generation |
| **LoRA** | Low-Rank Adaptation, parameter-efficient fine-tuning |
| **LLM** | Large Language Model |
| **Loss** | Measure of prediction error during training |
**M-R**
| Term | Definition |
|------|------------|
| **MoE** | Mixture of Experts architecture |
| **Multimodal** | Processing multiple data types (text, image, audio) |
| **Perplexity** | Exponential of cross-entropy, measures uncertainty |
| **Prefix Caching** | Reusing cached KV for common prefixes |
| **Prompt** | Input text given to an LLM |
| **Quantization** | Reducing numeric precision (FP16 → INT4) |
| **RAG** | Retrieval-Augmented Generation |
| **RLHF** | Reinforcement Learning from Human Feedback |
| **RoPE** | Rotary Position Embedding |
**S-Z**
| Term | Definition |
|------|------------|
| **SFT** | Supervised Fine-Tuning on instruction data |
| **Speculative Decoding** | Using draft model to accelerate generation |
| **System Prompt** | Instructions defining AI behavior |
| **Temperature** | Controls randomness in generation |
| **Token** | Subword unit processed by LLM |
| **Top-p** | Nucleus sampling parameter |
| **Transformer** | Neural network architecture with attention |
| **TTFT** | Time to First Token |
| **VLM** | Vision-Language Model |
| **Zero-shot** | Prompting without examples |
**Infrastructure Terms**
| Term | Definition |
|------|------------|
| **CUDA** | NVIDIA's GPU computing platform |
| **Flash Attention** | Memory-efficient attention algorithm |
| **HBM** | High Bandwidth Memory (GPU memory) |
| **NVLink** | High-speed GPU interconnect |
| **TensorRT** | NVIDIA inference optimization library |
| **vLLM** | High-throughput LLM serving engine |
| **GGUF** | File format for quantized models |
**Metrics**
| Term | Definition |
|------|------------|
| **BLEU** | Machine translation quality metric |
| **F1** | Harmonic mean of precision and recall |
| **Pass@k** | Code generation success probability |
| **TPOT** | Time Per Output Token |
| **WER** | Word Error Rate for speech recognition |