Cadabra Engineering Notes
Applied Intelligence in Practice: Engineering Notes by Cadabra
Technical patterns, practical insights, and AI-native approaches — from research to runtime.
👋 Welcome
This is our public knowledge space where we share reusable engineering concepts, AI-powered workflows, and lessons from the field. Most of what’s here is extracted from our real implementation work — refined across client projects, internal tools, and continuous experiments.
We publish what we would want to find ourselves: concise, context-rich, and ready to apply.
We believe we can reframe software delivery from the ground up, where every decision, tool, and interaction is guided by contextual intelligence.
📐 What’s Inside
🧠 AI Engineering Patterns
- LangChain orchestration
- vector database setups
- embedding workflows
- LLM chaining strategies
🛠️ DevOps & MLOps Templates
- CI/CD pipelines for models and APIs
- retraining logic
- rollback mechanisms
- hybrid infrastructure guides
🧩 Architecture Notes
- Modular system diagrams
- API-first layouts
- event-driven design using AI decision nodes
🎯 Prompt Engineering & Evaluation
- Structured prompt templates
- scoring methods
- context optimization techniques
🧪 Experiments & Benchmarks
- Real data on latency and accuracy
- prompt cost optimization
- multi-agent flows under load
🧭 How We Use This
Every entry in this space is:
- 🔍 Sourced from our Medium, LinkedIn, and Reddit posts
- 🧱 Based on real components, systems, or workflows we’ve shipped
- 🧾 Reviewed for reusability — no fluff, just practical tech
🗂️ How You Can Use It
- Reference system design ideas
- Reuse prompt logic or evaluation strategies
- Build on our API deployment patterns
- Copy/paste code snippets into your own infra
- Stay aligned with how modern AI delivery is evolving
📎 Tag Structure (Used Across Notes)
| Tag | Description |
|---|---|
#ai-pattern |
LLM usage models, chaining, embeddings |
#infra-snippet |
Deployments, Docker, FastAPI, pipelines |
#devops-note |
CI/CD, versioning, retraining strategies |
#prompt-logic |
Structured prompting and evaluation design |
#cadabra-core |
Internal methods we publicly share |
🧱 Format per Note
Each entry includes:
- Context & use case
- Architecture diagram (if applicable)
- Code block or config
- Linked source (Medium, LinkedIn, GitHub repo)
- Tags + Last reviewed timestamp
Let us know if you use any of these in production — or build something cooler with it.
We’re happy to link back.