2d ago · 5 min read · Introduction As part of improving my skills in data science, machine learning, and backend development, I decided to build a practical project that analyzes public opinion from social media. The idea
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3d ago · 14 min read · I built my search stack backwards—on purpose. Most teams start with retrieval and ranking, then try to bolt “understanding” onto the front once users complain that the system returns something, just not the thing they asked for. I did the opposite be...
Join discussion5d ago · 11 min read · TLDR: Supervised fine-tuning (SFT) is the stage where a pretrained model learns task-specific response behavior from curated input-output examples. It is usually the first alignment step after pretraining and often the foundation for later RLHF. Good...
Join discussion5d ago · 6 min read · The world of customer interaction is rapidly evolving, with AI VoiceBots at the forefront of this transformation. By 2025, these intelligent agents are not just answering queries; they're becoming integral to brand perception, operational efficiency,...
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5d ago · 13 min read · TLDR: Pre-training is the phase where an LLM learns "Language" and "World Knowledge" by reading petabytes of text. It uses Self-Supervised Learning to predict the next word in a sentence. This creates the "Base Model" which is later fine-tuned. 📖 ...
Join discussion5d ago · 13 min read · TLDR: Embeddings convert words (and images, users, products) into dense numerical vectors in a geometric space where semantic similarity = geometric proximity. "King - Man + Woman ≈ Queen" is not magic — it is the arithmetic property of well-trained ...
Join discussion5d ago · 13 min read · TLDR: An LLM doesn't "write" text — it generates a probability distribution over all possible next tokens and then uses a decoding strategy to pick one. Greedy, Beam Search, and Sampling are different rules for that choice. Temperature controls the c...
Join discussion5d ago · 12 min read · TLDR: Prompt Engineering is the art of writing instructions that guide an LLM toward the answer you want. Zero-Shot, Few-Shot, and Chain-of-Thought are systematic techniques — not guesswork — that can dramatically improve accuracy without changing a ...
Join discussion5d ago · 16 min read · TLDR: The Transformer is the architecture behind every major LLM (GPT, BERT, Claude, Gemini). Its core innovation is Self-Attention — a mechanism that lets the model weigh relationships between all tokens in a sequence simultaneously, regardless of d...
Join discussion5d ago · 13 min read · TLDR: At its core, GPT asks one question, repeated: "Given everything so far, what is the most likely next token?" Tokens are not words — they're subword units. The Transformer architecture uses self-attention to weigh how much each token should infl...
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