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Kwame Asante

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ML infrastructure engineer at a Lagos-based health-tech startup, focused on deploying LLMs on constrained hardware. Authored the open-source quantisation toolkit used by 40+ African AI teams.

aillmcloudperformance

Articles by Kwame Asante(8)

Grounding LLM outputs in source documents without losing flexibility

Grounding prevents hallucination but naive grounding sacrifices the reasoning quality that makes LLMs useful. The architecture that achieves both — faithful to sources, intelligent in synthesis — requires careful design of how context is provided and constrained.

aillmraggroundingbackend

22 July 2026

Building an LLM evaluation pipeline you can trust

Evaluating LLM outputs is harder than evaluating deterministic software outputs. The pipeline that works — one that catches regressions, measures what matters, and scales with the application — requires specific design choices from the beginning.

aillmtestingevaluationml-ops

22 July 2026

RAG pipelines that actually work in production

Retrieval-augmented generation is straightforward in demos and fragile in production. The gaps are almost always in chunking, retrieval quality, and context assembly — not in the language model. Here is what the production version looks like.

airagllmbackendproduction

22 July 2026

Tool use in production: why LLM agent reliability is a systems problem

An LLM agent that calls external tools is a distributed system. Its reliability is bounded by every tool it calls, every timeout it can hit, and every error it must handle gracefully. Building reliable agents requires systems thinking, not just prompt engineering.

aillmagentsbackendreliability

22 July 2026

Building a customer support bot that escalates gracefully

Learn how to design an LLM-powered customer support bot that handles common queries autonomously while detecting frustration, complexity, and edge cases that warrant a smooth handoff to human agents. This guide covers intent classification, escalation triggers, context preservation, and the handoff UX that keeps customers calm.

llmcustomer-supportchatbotsescalation

19 July 2026

Generating synthetic training data with LLMs

Synthetic data generated by LLMs can unblock model training when real labelled data is scarce, expensive, or sensitive — this guide walks through the practical techniques, quality controls, and pitfalls that matter in production

llmsynthetic-datamachine-learningdata-augmentationfine-tuning

19 July 2026

LLM cost tracking and budget controls in production

LLM API costs can spiral quickly without deliberate instrumentation. This article covers the full stack of cost tracking — from per-request token accounting to team-level budget enforcement — with concrete code patterns you can deploy today

llmcost-managementobservabilitycloudproduction

16 July 2026

Kwame Asante — ANN Tech — ANN Tech