How We Evaluate LLM Features Before They Ship

Golden datasets

We collect 100–500 real, anonymised inputs with expected outputs, reviewed by the client's domain experts. This becomes the regression test for every prompt or model change.

LLM-as-judge with human calibration

Automated grading on accuracy, tone, and safety, calibrated against human ratings each sprint so the judge stays trustworthy.

Cost and latency budgets

Every feature has a per-request cost and p95 latency ceiling. If a change blows the budget, it doesn't ship — regardless of quality gains.

Why this matters in 2026

Most AI features fail in production not because the model is weak but because the surrounding engineering — retrieval, evaluation, observability, cost control — is missing. This guide covers the practices we apply on every LLM project so features are reliable, measurable, and affordable at scale.

Step-by-step: how to approach it

1. Start with a golden dataset. 100–500 real, anonymised inputs with expert-approved outputs. Nothing ships without passing it.
2. Separate retrieval and generation. Measure whether the right context was found before judging the answer.
3. Version prompts like code. Prompts, tools, and configs live in git; CI runs the eval suite on every change.
4. Instrument everything. Log prompts, retrieved chunks, tool calls, latency, and token cost with request IDs.
5. Set budgets. Per-request cost and p95 latency ceilings are release gates, not dashboards.

At a glance

Common mistakes to avoid

• Testing on a handful of hand-picked examples and calling it evaluation.
• Upgrading models without re-running evals — regressions are common.
• Chunking documents by fixed character count and losing tables and headings.
• No caching, so identical requests are paid for repeatedly.
• Treating hallucination as a model problem when it is usually a retrieval problem.

How FreshCodes can help

We are an AI-first development company that designs, builds, and operates AI agents, LLM-powered applications, and AI-native web and Flutter mobile products. Every engagement starts with a free discovery call where we map your process, assess your data, and give you a fixed-scope plan with a timeline and estimate — including projected running costs. From there we ship weekly, measure against an evaluation set, and support your product after launch.

• AI Agent Development — autonomous agents and copilots for support, sales, operations, and HR
• AI & ML Integration — add LLM features to your existing product without a rebuild
• Data Engineering & Analytics — AI-ready pipelines, warehouses, and conversational analytics
• AI Mobile App Development and AI-Powered Web Development — Flutter, React, Node.js, Laravel