Category
generative-ai
10 articles in this category
Mastering Prompt Templates: System, User, and Assistant Roles with LangChain
TLDR: A production prompt is not a string ā it is a structured message list with system, user, and optional assistant roles. LangChain's ChatPromptTemplate turns this structure into a reusable, testable, injection-safe blueprint. š The API Contrac...
Prompt Engineering Guide: From Zero-Shot to Chain-of-Thought
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 ā not guesswork ā that can dramatically improve accuracy without changing a ...
Multistep AI Agents: The Power of Planning
TLDR: A simple ReAct agent reacts one tool call at a time. A multistep agent plans a complete task decomposition upfront, then executes each step sequentially ā handling complex goals that require 5-10 interdependent actions without re-prompting the ...
How to Develop Apps Using LangChain and LLMs
TLDR: LangChain is a framework that simplifies building LLM applications. It provides abstractions for Chains (linking steps), Memory (remembering chat history), and Agents (using tools). It turns raw API calls into composable building blocks. š L...
How GPT (LLM) Works: The Next Word Predictor
TLDR: At its core, GPT asks one question, repeated: "Given everything so far, what is the most likely next token?" Tokens are not words ā they're subword units. The Transformer architecture uses self-attention to weigh how much each token should infl...
Diffusion Models: How AI Creates Art from Noise
TLDR: Diffusion models work by first learning to add noise to an image, then learning to undo that noise. At inference time you start from pure static and iteratively denoise into a meaningful image. They power DALL-E, Midjourney, and Stable Diffusio...
AI Agents Explained: When LLMs Start Using Tools
TLDR: A standard LLM is a brain in a jar ā it can reason but cannot act. An AI Agent connects that brain to tools (web search, code execution, APIs). Instead of just answering a question, an agent executes a loop of Thought ā Action ā Observation unt...
LLM Hyperparameters Guide: Temperature, Top-P, and Top-K Explained
TLDR: Temperature, Top-p, and Top-k are three sampling controls that determine how "creative" or "deterministic" an LLM's output is. Temperature rescales the probability distribution; Top-k limits the candidate pool by count; Top-p limits it by cumul...
RAG Explained: How to Give Your LLM a Brain Upgrade
TLDR: LLMs have a training cut-off and no access to private data. RAG (Retrieval-Augmented Generation) solves both problems by retrieving relevant documents from an external store and injecting them into the prompt before generation. No retraining re...
Variational Autoencoders (VAE): The Art of Compression and Creation
TLDR: A VAE learns to compress data into a smooth probabilistic latent space, then generate new samples by decoding random points from that space. The reparameterization trick is what makes it trainable end-to-end. Reconstruction + KL divergence loss...