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How does Model Quantization work?
Model Quantization stores and computes a model's numbers at lower precision, so the model takes less memory and runs faster, even on a laptop or a phone.
How does Chain-of-Thought (CoT) Prompting work?
Chain-of-Thought (CoT) Prompting is a technique where we ask the model to write out its reasoning steps before the final answer, which makes it more accurate.
How does Prompt Chaining work?
Prompt Chaining breaks one big task into smaller prompts, where the output of one prompt becomes the input of the next. It solves bigger tasks reliably.
How does PyTorch work?
PyTorch is an open-source library to build and train machine learning models. It uses tensors, a computation graph, and autograd, and runs fast on the GPU.
How does Semantic Caching work?
Semantic Caching is a cache that matches questions by their meaning instead of their exact words, so an AI app can reuse past answers for similar questions.
How does Hybrid Search work?
Hybrid Search combines keyword search and semantic search, and merges their results into one final ranked list, so that we get the best of both.
How does HyDE work in RAG?
HyDE (Hypothetical Document Embeddings) is a RAG technique where the AI first writes a fake answer to the question, and then we search using that fake answer.
Prefill vs Decode: LLM Inference Optimization
Prefill reads the whole prompt in one pass and produces the first token, whereas Decode generates the rest of the tokens one at a time using the KV cache.
How does a GPU work for Deep Learning?
A GPU is a chip that does a huge number of simple calculations at the same time. Deep learning is mostly matrix multiplication, so GPUs make it very fast.