Large Languagle Models (LLMs) & Natural Language Processing (NLP)

Est. read time: 2 minutes | Last updated: March 09, 2026 by John Gentile

Contents

Overview

ChatGPT - OpenAI
Claude - Anthropic
Gemini - Google, which also has other tools like AI Studio which also has a very large context window.
Grok - xAI

LLMs have varied abilities when it comes to coding though Claude Code has been my favorite for awhile.

Claude Code Docs
Dammyjay93/claude-design-engineer: Design engineering for Claude Code. Craft, memory, and enforcement for consistent UI.
yamadashy/repomix: packs an entire repository into a single, AI-friendly file, which can then be uploaded to most LLMs.

ChatGPT’s Sora is great for realistic and photo image generation.
Most general models can synthesize an image from a prompt if the result is a text-based image format such as SVG, Mermaid, HTML/CSS, etc.

Suggestions:

Start with a short and simple prompt, and iterate to get better results.
Put instructions at beginning or end of prompt, while clearly separating instructions from the text of interest.
Describe how the model should behave and respond- for example, if looking for coding advice, can create a system prompt of You are a helpful assistant that answers programming questions.
- Add specificity and descriptions about the task and desired output, as well as including examples of output if possible.
- Instructions should tell what “to do” rather than “what not to do”.

References:

Ollama: easily run LLMs locally. Very easy to setup and start.
- llama.cpp: fast, low overhead inference of LLMs in C/C++ that runs under-the-hood of Ollama.
Hugging Face: pre-trained NLP models & reference
LangChain: LangChain is a Python framework for developing applications powered by large language models (LLMs).