Everyone talks about the Neural Network, but the Tokenizer is the unsung hero of LLMs. This post explains what a Tokenizer actually does, why we use Byte Pair Encoding (BPE), and how these tokens bridge the gap between rigid integers and meaningful vector embeddings in models like GPT-4.
High-bandwidth input is the bottleneck of modern computing. Voice agents are for delegation; Voice dictation is for creation. This post explores why we need ‘Agentic Dictation’ to match the speed of our thoughts.
Conventional keyword search matches user query words to document words using an inverted index data structure for efficient matching and ranking by relevancy.
Retrieval is the backbone of RAG. We explore the critical steps often missed by developers: proper chunking strategies, the ‘Librarian’ analogy for vector vs. keyword search, and solving the math problem of Hybrid Search using Reciprocal Rank Fusion (RRF).
LLMs revolutionize question-and-answer systems with exceptional language understanding and creative writing skills. Lossy compression during training may make retrieving information challenging. Leveraging LLMs’ language expertise transforms building question-and-answer systems into reading comprehension using their ability to comprehend text, forming the basis for RAG systems that shift question answering to efficient knowledge base searches.