Build an Eval Suite Before Optimising Prompts
Stop guessing whether a change improved anything.
/ quick answer
Create a scored dataset first, then treat every change as an experiment against it. Stop guessing whether a change improved anything.
- 01Collect 30–50 real inputs, weighted toward known failures.
- 02Define the expected outcome per case: required facts, structure, forbidden content.
- 03Choose a scorer per case type: assertion for structure, rubric for quality.
- 04Record a baseline score before changing anything.
- 05Run the suite on every prompt, model or retrieval change; compare against baseline.
- 06Add every new production failure to the dataset the day it happens.
/ frequently asked
What does the Build an Eval Suite Before Optimising Prompts workflow do?
Create a scored dataset first, then treat every change as an experiment against it.
What problem does Build an Eval Suite Before Optimising Prompts solve?
Teams tweak prompts based on a handful of eyeball tests and ship regressions they never see.
How many steps does Build an Eval Suite Before Optimising Prompts take?
6 steps. It starts with collect 30–50 real inputs, weighted toward known failures. and ends with add every new production failure to the dataset the day it happens..
How do I set up Build an Eval Suite Before Optimising Prompts step by step?
1) Collect 30–50 real inputs, weighted toward known failures. 2) Define the expected outcome per case: required facts, structure, forbidden content. 3) Choose a scorer per case type: assertion for structure, rubric for quality. 4) Record a baseline score before changing anything. 5) Run the suite on every prompt, model or retrieval change; compare against baseline. 6) Add every new production failure to the dataset the day it happens.
Which tools does Build an Eval Suite Before Optimising Prompts need?
It uses ai-observability-stack, ai-testing-stack — each linked below with its own node.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →AI Evaluation
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.
- →AI Testing
AI testing covers two things: using AI to generate and maintain tests, and testing AI systems whose output is non-deterministic.
- →AI Monitoring
AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift.
- →Agent Cost Control
Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
Related workflows
Turn this into a repeatable process.
- →Harden an AI System Against Injection and Misuse
Architectural controls that survive a manipulated model.
- →Build an MCP Server for Your Own App
Expose your product's capabilities to every AI client with one server.
- →Build a Research Automation Pipeline
Question in, sourced structured brief out — on a schedule.
- →Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
Related tool stacks
The tools that run it in production.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
- →AI Testing Stack
Test deterministic code and probabilistic AI output in one pipeline.
- →AI Security Stack
Least-privilege tooling, approval gates and audit trails for agentic systems.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
Related prompts
Reusable prompts for this job.
- →Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
- →Model Routing Classifier Prompt
A cheap-model prompt that labels request difficulty so it can be routed to the right model.
- →Red Team Attack Plan Prompt
Generates a tailored list of adversarial tests for your own AI feature.
Related use cases
How people apply it, and what came out.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
Comparisons & alternatives
Pick between the options.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.