Est. read time: 2 minutes | Last updated: May 28, 2025 by John Gentile


Contents

Overview

Large Language Models (LLM)

LLMs for Coding

LLMs for Research

Claude, OpenAI/ChatGPT and Grok have “research” modes where the model is open to searching the internet, and has multi-step reasoning to come up with deep assessments and recommendations.

Prompt Engineering

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:

Self-Hosted

  • 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).

Other Uses/Automation

  • LaurieWired/GhidraMCP: ghidraMCP is an Model Context Protocol server for allowing LLMs to autonomously reverse engineer applications. It exposes numerous tools from core Ghidra functionality to MCP clients.

Architecture & References