CodingAdvanced

LLM evaluation before production

Compare models and prompts against a fixed task set before an AI feature reaches customers, with traces, cost limits, and human release approval.

Setup time

4 hours

Time saved

4-10 hours

Best for

AI product teams, Developers, Technical founders, Platform teams

Tools

OpenRouter, LangSmith, Langfuse, Codex, DeepSeek

Quick answer

How does the “LLM evaluation before production” workflow work?

This workflow prevents model selection by demo alone. It evaluates a real task set for accuracy, safety, latency, cost, and operator correction before a production release. It takes about 4 hours, uses OpenRouter, LangSmith, Langfuse, Codex, DeepSeek, and follows 5 documented steps.

Published by GPTNavi Editorial TeamLast materially updated 2026-08-19

Built from public product information and practical workflow-design patterns. Verify current pricing, features, and policies with each provider.

Overview

This workflow prevents model selection by demo alone. It evaluates a real task set for accuracy, safety, latency, cost, and operator correction before a production release.

When to use this workflow

AI support drafts

Document extraction

Internal knowledge assistant

Classification workflow

Tools you need

OpenRouter

Developer automation

Freemium

Unified API and model marketplace for testing, routing, and operating many AI models through a consistent interface.

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LangSmith

Developer automation

Freemium

Platform for tracing, evaluating, testing, and monitoring LLM applications and agent workflows.

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Langfuse

Developer automation

Open source

Open-source LLM observability and evaluation platform for traces, prompts, datasets, quality review, and cost tracking.

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Codex

AI coding

Freemium

OpenAI's agentic coding workspace for parallel implementation, code review, testing, and recurring engineering work.

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DeepSeek

AI assistant

Free

AI assistant and model platform for reasoning, coding, document analysis, and low-cost experimentation.

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Step-by-step workflow

Build a representative test set

Collect anonymized examples that cover routine, ambiguous, adversarial, missing-data, and edge-case requests with expected outcomes.

Tool used

LangSmith

Expected output

A versioned evaluation dataset.

Run model comparisons

Test approved models through a consistent interface and record output quality, latency, token use, and failure cases.

Tool used

OpenRouter

Expected output

Comparable model runs.

Trace real workflow behavior

Capture prompts, tool calls, latency, cost, and errors in a controlled pre-production environment.

Tool used

Langfuse

Expected output

A trace and observability baseline.

Fix the workflow, not just the prompt

Use a coding agent to implement input validation, source requirements, approval gates, retries, and safe fallbacks.

Tool used

Codex

Expected output

A hardened feature branch.

Run final challenge cases

Use a second model as one reviewer for missed risks, then have a human owner approve the release decision.

Tool used

DeepSeek

Expected output

A signed release checklist.

Prompt templates

Evaluation rubric

Create an evaluation rubric for this AI feature. Include accuracy, citation or evidence quality, safety, privacy, refusal behavior, latency, cost, correction burden, and escalation. Feature: [paste]

Failure analysis

Analyze these AI failures. Group root causes into input quality, prompt design, retrieval, tool use, model limits, unsafe action, and UX ambiguity. Recommend the smallest reliable fix and a regression test. Runs: [paste]