# System prompts Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/system-prompts
> Markdown URL: https://aitinkerers.org/technologies/system-prompts.md
> Technology record last updated: 2026-02-24T00:23:44Z
> Generated: 2026-09-21T12:41:06Z

System prompts are the foundational, persistent instructions that define an LLM's persona, constraints, and overall behavioral alignment.

System prompts function as the 'BIOS' configuration for a Large Language Model (LLM): they are a foundational instruction set, loaded once, that dictates the model's core conduct (e.g., 'You are a concise, trustworthy summarization engine'). Unlike a user prompt, the system prompt remains constant across all interactions, ensuring consistency. Developers use this mechanism to set specific guardrails, assign a role (e.g., 'expert TypeScript engineer'), and enforce output formatting (e.g., 'always use Markdown for code blocks'). This steerability is critical for production-grade AI applications, directly impacting response reliability and brand voice adherence.

- Official technology site: https://www.promptingguide.ai
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Building a Persistent Memory & Stateful Second Brain AI Agent](https://dhaka.aitinkerers.org/talks/rsvp_kSMaLnvvGGg)

Demonstrating context engineering in practice through Slatekore - an open-source starter kit that gives Gemini CLI persistent memory using Obsidian as the storage layer. 1. State Management Without Infrastructure How to use the file system as persistent state - your Obsidian vault becomes both the agent's memory and its knowledge base. No databases, no vector stores, no MLOps. 2. Context Engineering for Agent Behavior Crafting system prompts (GEMINI.md) and workflow files that define agent capabilities, constraints, and multi-step reasoning patterns. The prompt is the agent's configuration. 3. Tool Use Through Natural Interfaces Connecting the agent to real actions: creating files, managing tasks, building knowledge graphs - all through natural language commands backed by structured templates. Watch a stateful agent capture research, recall context from previous sessions, update its knowledge graph, and execute project workflows - without any model training or fine-tuning.

- Event context: AI Tinkerers Dhaka 2nd Meetup: Dhaka Builds with AI! — 2026-02-07 — Dhaka
- Public talk page: https://dhaka.aitinkerers.org/talks/rsvp_kSMaLnvvGGg

### [Teaching Generative AI to K-12 Students](https://seattle.aitinkerers.org/talks/rsvp_hH1rzLAN0ak)

In this demo, I'll showcase the work we've been doing at Code.org, creating a Generative AI curriculum for students (due for release end of Oct). The demo will show how students can use Code.org to explore SLMs (Mistral), select different fine-tuned models, learn about system prompts, temperature, model cards, RAG, and other aspects of Generative AI. I'll also cover how we've implemented a "guard LLM" to ensure a safe student experience in the classroom.

- Event context: AI Tinkerers - Seattle - October 2024 Meetup — 2024-10-25 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_hH1rzLAN0ak

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