Category
generative ai
10 articles across 2 sub-topics
Mastering Prompt Templates: System, User, and Assistant Roles with LangChain
TLDR: A production prompt is not a string — 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. TLDR: LangChain p...
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 — not guesswork — 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 — 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. TLD...
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 — 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 → Action → 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...