Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Learn how LLMs break text into tokens using BPE, WordPiece, and SentencePiece algorithms, and how tokenization impacts cost, performance, and application design.
A clear, code-driven explanation of the transformer architecture including self-attention, multi-head attention, positional encoding, and how encoder-decoder models work.
Understand context windows in LLMs — what they are, how they differ across models, and practical strategies for building applications that work within token limits.
Master the sampling parameters that control LLM behavior — temperature, top-p, top-k, frequency penalty, and presence penalty — with practical examples showing when to use each.
Learn the complete LLM training pipeline from pre-training on internet-scale data through supervised fine-tuning and RLHF alignment, with practical code examples at each stage.
A practical comparison of the major foundation models — GPT-4, Claude, Gemini, Llama, and Mistral — covering capabilities, pricing, context windows, and guidance on when to use each.
Understand the autoregressive generation process, KV cache optimization, batching strategies, and the latency vs throughput trade-offs that govern LLM inference performance.
Learn what embeddings are, how they capture semantic meaning as vectors, how to use embedding models for search and clustering, and the role cosine similarity plays in AI applications.
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