Mar 4 · 6 min read · Abstract - The fastest way to fail with AI is to pick a model by reputation and hope. This guided journey walks you from a typical, broken selection process into a repeatable, production-ready decision path. Rather than a checklist, you'll fol...
Join discussionMar 3 · 5 min read · Abstract: the hidden assumption that undermines reliability As a Principal Systems Engineer, the task here is to peel back layers until the assumptions that quietly govern production behavior are visible and actionable. The point is not to r...
Join discussionFeb 23 · 5 min read · Large foundation models promise to accelerate development, but teams keep hitting the same wall: a high-performing model in one project becomes brittle or expensive when reused elsewhere. That gap between research-grade performance and reliable produ...
Join discussionFeb 23 · 6 min read · Before: the rough way teams picked models Teams often start from the obvious trap: chase a brand name, run a single prompt, and pick the answer that “feels” best. That quick judgment ignores latency budgets, prompt stability, cost-per-cal...
Join discussionFeb 20 · 5 min read · Discovery - the plateau that became a crisis As the senior solutions architect accountable for a customer-facing conversational platform, the problem was simple to state and painful to watch: throughput stalled while costs rose, and the system...
Join discussionFeb 17 · 6 min read · Then vs. now: the industry used to treat model selection as a checkbox-bigger meant better, and teams defaulted to the largest available model without deeply questioning fit. That logic still shows up in benchmarks and vendor demos, but increasingly ...
Join discussionFeb 12 · 5 min read · Modern conversational systems often look reliable in demos, then start failing quietly in real usage: answers lose context, facts slip, and behaviour becomes inconsistent across sessions. This is the core operational problem many teams face when they...
Join discussionFeb 11 · 6 min read · Abstract - A guided journey from chaos to clarity. Teams scramble with a dozen model specs, cost estimates, and anecdotal performance claims. Benchmarks live in spreadsheets, latency surprises surface in production, and the "best" model on paper crum...
Join discussionFeb 10 · 5 min read · I used to hop between whatever model was hyped that week. One day a fast responder handled my prototype prompts beautifully, the next day a different engine gave cleaner code. That felt efficient until the team hit an integration wall-latency spikes,...
Join discussionFeb 9 · 5 min read · Abstract - As the lead solutions architect responsible for a high-throughput conversational AI pipeline, this case study documents a focused migration that resolved a multi-month operational plateau. The system handled mixed channels (chat, email, an...
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