LLM Engineering Lab

Motivation

This lab is my sandbox for building reliable LLM capabilities in Python: clear interfaces, controlled generation, and outputs that are immediately usable in real workflows. The emphasis is on engineering judgement — when to break a task into multiple steps, how to constrain prompts so that behaviour stays consistent, and how to produce artefacts that can be reused downstream.

What it delivers

A toolkit of reusable utilities that turn messy inputs into clean artefacts:

• Web Summary Tool — Converts long, unstructured webpages into concise Markdown briefs so teams can extract decision-relevant information quickly and consistently.
• Company Brochure Generator — Produces a customer/investor/recruit-ready company brief by selecting the most relevant site pages and synthesising them into a structured Markdown brochure.
• Tech Tutor — Delivers fast, high-clarity explanations of data/ML/software concepts and code to reduce ramp-up time, unblock implementation, and improve technical decision-making.

Engineering

• Workflow-ready outputs: Results are produced as Markdown and structured JSON so they can be pasted into documentation, tickets, and internal wikis, or passed downstream without manual cleanup.
• Reliable multi-step pipelines: Where it improves quality, the system separates “decide what to read” from “write the final output”, reducing noise and keeping generations grounded in the right context.
• Prompt contracts: Prompts are treated as specifications, with explicit control over tone, length, and formatting so outputs stay consistent and user-friendly.
• Environment portability: The same interfaces can run against hosted or local models, making it easy to use the tools across different setups and constraints.

Links & Resources

• Code repository: GitHub – LLM Engineering Lab