2 posts tagged with "Task Specific LLM Evaluation"

Introduction: The Hidden Risk in AI Consulting​

Over the past year, choosing a large language model has become one of the most important decisions in building AI-powered products. Yet in many AI consultancies, that decision is still made in surprisingly informal ways — defaulting to the latest frontier model, running a few prompts, and moving quickly into production.

That approach can work in internal teams where decisions are easy to iterate on and rarely scrutinised. But consulting is different. When you are building on behalf of a client, every technical choice becomes a recommendation that must stand up to questioning, both now and in the future.

Model selection is no longer just a technical preference. It is a decision that affects cost, performance, and trust — and increasingly, one that needs to be justified with evidence.

Key Takeaways​

• Comparing large language models (LLMs) for production involves balancing trade-offs between quality, cost, latency, and reliability, measured on your specific workloads rather than relying on public leaderboards.
• Begin with a small, representative evaluation set of 50–200 real examples drawn from production traffic, scaling up as decisions become more critical or costly.
• Fair comparisons require consistent prompts, inference settings, and clear evaluation criteria across all AI models.
• Use a gate-based decision process: first eliminate models that fail minimum thresholds for quality, latency, or reliability, then select remaining candidates based on cost and secondary metrics.
• Establish ongoing, repeatable evaluation harnesses to detect regressions over time, following a structured workflow aligned with science-grade LLM evaluation experiments.