Advance Planning for AI Project Evaluation

Source: Towards Data Science

Evaluating AI Projects Before You Build

When a new AI‑powered product or feature is proposed—say, an LLM‑based agent—Product and Engineering teams quickly start brainstorming use‑cases and hype.

If you’re in that room, the first question you should ask is:

“How are we going to evaluate this?”

Sometimes this triggers a debate: Is AI evaluation really necessary? Can we postpone it (or skip it altogether)?

The Bottom Line

You only need AI evaluations if you want to know whether it works. Shipping without any assessment means you’re flying blind on impact to the business and to customers—something most organizations can’t afford.

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What You Need Before You Start Building AI

1. Clear Success Metrics

   • Business‑level KPIs (e.g., conversion rate, churn reduction, revenue uplift)
   • Product‑level metrics (e.g., task completion time, error rate)
   • User‑experience metrics (e.g., satisfaction score, Net Promoter Score)

2. Baseline Measurements

   • Capture current performance of the existing system or manual process.
   • Establish a “ground truth” dataset for later comparison.

3. Evaluation Framework

   • Quantitative: accuracy, precision/recall, F1, BLEU, ROUGE, latency, cost per request.
   • Qualitative: human‑in‑the‑loop reviews, usability testing, bias audits.
   • Safety & Compliance: privacy impact assessment, regulatory checks, robustness tests.

4. Data Strategy

   • Identify the data needed for training, validation, and testing.
   • Ensure data quality, representativeness, and proper labeling.
   • Plan for ongoing data collection to monitor drift.

5. Experiment Design

   • Choose A/B testing, canary releases, or offline simulations.
   • Define sample size, duration, and statistical significance thresholds.

6. Stakeholder Alignment

   • Document who owns each metric and who will act on the results.
   • Set up regular review cadence (e.g., weekly dashboards, post‑launch retrospectives).

7. Risk Mitigation Plan

   • Outline fallback mechanisms if the model underperforms.
   • Prepare a rollback strategy and communication plan for users.

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Quick Checklist

TL;DR

If you want confidence that your AI feature delivers value, you must plan its evaluation before you start building. Skipping this step is a gamble most businesses can’t afford to take. Use the checklist above to ensure you’re ready to measure success from day 1.

The Objective

What is the AI supposed to do?

• Define its purpose.
• Visualize the end‑state when it works as intended.

Why This Matters

Many teams start building AI products without a clear answer to the above questions. Without a shared vision:

• It’s hard to set meaningful success metrics.
• Misaligned expectations surface later, turning into costly conflicts.

What Happens When the Goal Isn’t Defined

1. Scope creep – AI is added “because it’s valuable,” not because it solves a specific problem.
2. Internal disagreement – One stakeholder may feel the project succeeded while another sees failure.
3. Wasted resources – Time, energy, and budget are spent before the misalignment is discovered.

How to Avoid the Mess

• Agree upfront on the AI’s objective.
• Document the desired outcome and success criteria.
• Communicate this vision across all stakeholders before any development begins.

By establishing a clear, shared objective early, you set the foundation for measurable success and smoother collaboration throughout the project.

KPIs

It’s not enough to simply imagine a scenario where an AI product or feature works. That vision must be translated into measurable forms—such as key performance indicators (KPIs)—so we can later build the evaluation tooling needed to calculate them.

• Qualitative data (e.g., “sniff tests”) can add color, but relying solely on ad‑hoc user trials without a systematic plan won’t generate enough reliable information to generalize about product success.
• Vibes like “it seems ok” or “nobody’s complaining” are lazy and ineffective.

Why Systematic Measurement Matters

1. Statistical significance – Gathering enough data to form a statistically significant picture can be costly and time‑consuming, but it’s far better than pseudoscientific guessing.
2. Representativeness – Spot checks or volunteered feedback are rarely representative of the broader user experience. Many users don’t proactively share their experiences, good or bad.
3. Test case rigor – Test cases for an LLM‑based tool can’t be invented on the fly. You must:
   • Identify the usage scenarios you care about.
   • Define tests that capture those scenarios.
   • Run the tests enough times to be confident in the range of results.

Defining and executing these tests will happen later, but the identification of usage scenarios and initial KPI planning should start now.

Set the Goalposts Before the Game

Thinking about assessment and measurement up front helps prevent you and your team from (explicitly or implicitly) “gaming the numbers.” Defining your KPIs after the project is built—or after it’s deployed—often leads to choosing metrics that are easier to measure or easier to achieve, rather than those that truly reflect success.

In social‑science research this tension is captured by the concept of measurement validity: the difference between what you can measure and what actually matters.

Why Measurement Validity Matters

• Define the construct – Clearly articulate what “success” looks like (e.g., health, user satisfaction, productivity).
• Decompose the construct – Break it into its component parts and identify appropriate indicators for each.
• Avoid proxy shortcuts – Using a single, convenient proxy (e.g., BMI for health) may be cheap and easy, but it rarely captures the full concept and thus lacks validity.

Example: Measuring Health

Practical Steps Before Development

1. Articulate a concrete vision of success in practical, observable terms.
2. Translate that vision into measurable objectives (your initial KPIs).
3. Break down each KPI into more granular sub‑metrics if needed.
4. Document the rationale for each metric to guard against later “goal‑post moving.”

Until the AI tool’s development begins, there will always be unknowns. The best you can do is set clear, defensible goalposts now and commit to them throughout the project.

Think About Risk

Why Start with a Risk Conversation?

• Alignment early on: Discussing risk tolerance at the beginning helps surface differing viewpoints before the project gains momentum.
• Influences success criteria: The way you define success may shift once you understand the organization’s comfort with uncertainty.
• Shapes testing strategy: Knowing the acceptable risk level guides the design of validation and monitoring tests later in the workflow.

The Nature of LLMs

• Nondeterministic behavior: The same prompt can yield different responses across runs.
• Implications for business:
  • Occasionally the model may produce novel, undesirable, or simply odd outputs.
  • You cannot guarantee that an AI agent will behave exactly as expected every time.

Managing the “Hundredth‑Case” Risk

1. Identify failure modes: Catalog the types of errors or unexpected behaviors you might encounter.
2. Assess impact: Determine the potential business, legal, or reputational consequences of each failure mode.
3. Set acceptance thresholds: Decide which risks are tolerable and which require mitigation.
4. Incorporate into AI assessment: Use the above analysis to inform your overall AI risk‑assessment framework and monitoring plan.

By front‑loading this conversation, you create a shared understanding of what “acceptable risk” looks like, enabling more focused development, testing, and governance of LLM‑driven solutions.

Conclusion

This might feel like a lot—I’m giving you a whole to‑do list before anyone has written a line of code! However, evaluation for AI projects is more important than for many other types of software projects because of the inherent nondeterministic character of LLMs I described.

Producing an AI project that generates value and improves the business requires close scrutiny, planning, and honest self‑assessment about what you hope to achieve and how you will handle the unexpected. As you proceed with constructing AI assessments, you’ll think about the kinds of problems that may occur (hallucinations, tool misuse, etc.) and how to detect them—both to reduce their frequency and to be prepared when they do arise.

Read more of my work at stephaniekirmer.com