AI Engineering
6 articles in this topic.
Building RAG Over Your ERP Data: The Architecture That Actually Survives Production
Most ERP chatbots fail the moment someone asks for a total. Here is the two-plane architecture, the hybrid retrieval SQL, the reranking step and the permission model that separate a demo from a system finance will actually trust.
Self-Hosting Open-Weight Models: The VRAM and Cost Math Nobody Shows You
Before you buy a GPU, do the arithmetic. Weights, KV cache, quantisation, batching and bandwidth decide whether self-hosting beats an API - and for most African teams the honest answer is a hybrid, not a rack.
Prompt Injection Is the New SQL Injection - And Most African AI Projects Are Wide Open
OWASP ranks prompt injection as the number one risk in LLM applications, and there is still no complete fix. Here is how direct and indirect injection actually work, and the defence-in-depth pattern that limits the blast radius.
Stop Regex-Parsing LLM Output: Structured Outputs, JSON Schema and Constrained Decoding
The retry-until-it-parses loop was never engineering. Constrained decoding makes schema conformance a property of the sampler, not a hope. Here is how it works, how to use it on hosted and local models, and the traps that remain.
How to Evaluate an LLM Feature Before It Touches a Customer
Vibes are not a release gate. Build the eval set from real traffic, split deterministic checks from LLM-as-judge scoring, calibrate the judge against humans, and wire the whole thing into CI so a prompt change cannot silently regress.
Agentic Coding as an Engineering Discipline: My Daily Claude Code Workflow
An agentic coding tool is not autocomplete. It is a process that runs unattended, so the engineering is in the verification loop, the context budget and the repo scaffolding. Here is the setup and the daily rhythm that actually works.