Structured thinking, modular inputs, explainable outputs.
A personal lab for designing and evaluating context-aware workflows for LLM systems.
Context Engineering Lab is a structured collection of design experiments around:
- 🧱 Structured Input Design (e.g. multi-modal prompt templates)
- 🔍 Vector Search & Retrieval (e.g. semantic chunking, FAISS)
- 🧠 LLM Integration (e.g. reasoning tasks, RAG pipelines)
- 📊 Prompt Evaluation (human-in-the-loop + automated metrics)
Each module explores a different angle of making LLMs more grounded, interpretable, and production-ready, especially in security, behavior modeling, and explainability-critical settings.
| No. | Module | Description |
|---|---|---|
| 01 | Context Graph | Build reasoning flow via structured context graph |
| 02 | Retrieval-Enhanced Prompting | Boost response quality using vector-based retrieval |
| 03 | Structured ATO Evaluation | Evaluate structured input pipelines for account takeover detection |
| 04 | Agent Routing via Structured Context | Select and orchestrate agents based on parsed semantic schema |
- Explore prompt architecture from a systems + design perspective
- Apply consulting-style reasoning (CCE: Complete, Conclusive, Explainable) to LLM workflows
- Build and test patterns that improve grounding, control, and downstream integration
This lab does not focus on:
- Fine-tuning LLMs
- Proprietary tooling or closed-source platforms
- General prompt tips — this is about structured, testable input design
Each module includes:
- ✅ Markdown design doc (in
/modules) - 📓 Optional notebooks (in
/notebooks) - 🧪 Prompt templates + sample outputs (in
/examples) - 📊 Evaluation plans or logging scripts (in
/eval)
👉 Try it: matcha_prompt_blueprint_demo.ipynb