Stop asking which one is best. Ask which one fits your data shape, latency budget, and team skill set.
Cutting through the noise
Every six months a new framing of AI capability becomes the dominant narrative. Each is partly true and largely overstated. The practical question for teams shipping software is simpler — which capabilities are reliable enough to bet a customer experience on, and which still need a human in the loop?
- Reliable today: classification, extraction, summarisation in bounded domains.
- Reliable with care: retrieval-augmented generation, structured output.
- Not yet reliable: open-ended autonomous decision-making with high consequence.
The architecture patterns that work
After deploying LLM applications for clients across health, finance and e-commerce, the patterns that ship to production look remarkably similar. A clear retrieval layer. Aggressive evaluation harnesses. Human-in-the-loop for high-consequence decisions. Boring infrastructure underneath.
- Retrieval is the lever, not the model. Most failures originate in search, not generation.
- Evals as a first-class engineering artefact, not a research nice-to-have.
- Cost and latency budgets defined up front, not discovered after launch.
Magic in the demo. Engineering in production. The gap between the two is where teams either ship or stall.
What the next twelve months will demand
The teams that win the next phase will be the ones that operationalise AI rather than experiment with it. That means dull but important things — versioned prompts, regression suites, cost dashboards, fallback paths. The magic is real. So is the engineering.
Where this leaves us
If you have read this far, you probably already know whether this thinking maps to your situation or not. We are not interested in convincing you. We are interested in working with the small number of teams who arrive at conclusions like these on their own and then need a partner who can help them act.
If that is you — for ai work or anything adjacent — come and talk to us. The first conversation is always on us, and it always goes somewhere useful.
Written by Priya Ramanathan for the Lilizon Journal. Published 2025-01-17. Filed under AI.