You've shipped a demo, raised funding, maybe even landed your first few customers, but:

• Your AI system doesn’t always work

• Customers are getting frustrated

• You’re not sure how to debug or improve things

• And you're stuck in “maybe we should change the model again?” model

Most companies would start conducting big changes and hoping for the best.

Just by using data, feedback, you can improve your AI solution becuse you will know where the root cause of the problem is.

How To Build a Simple Evaluation System That Actually Works

• Helps you find and fix real problems

• Works whether you're using RAG or just a pure LLM

• Gives you signals that lead to real improvements

• Can be run weekly without stress

Step 1: Start With Your Objective

Before metrics, models, or dashboards — ask the real questions:

• What does this AI system do?

• Who is it for?

• What does a “good” response look like?

• What are the failure modes we want to avoid?

Step 2: Create a Synthetic Test Data Set

You don’t need a million rows of data. You need 20–50 realistic, representative test cases.

Each entry should include:

• A realistic user query

• The ideal answer you want

• (For RAG systems) The documents that answer should come from

Use GPT to generate drafts, then manually edit for quality.

This becomes your golden dataset — your test bed for all future changes.

You can either

Step 3: Build a Simple CLI or Script to Run Evals

You don’t need any expensive software. Just write a script that:

• Takes the test queries

• Sends them through your system (prompt → retrieval → model)

• Logs the:

  • Query

  • Retrieved documents

  • Generated answer

  • Time to respond

  • Evaluation scores

Now you can test your AI system with every update — and compare results over time.

Step 4: Start With Basic Retrieval Metrics (If Using RAG)

If your AI depends on retrieved documents (like in RAG), you must start by checking retrieval.

Two essential metrics:

• Retrieval Precision@k – What % of retrieved docs are relevant?

• Retrieval Recall@k – What % of all relevant docs were retrieved?

If your system is pulling garbage, your answers will be garbage too, no matter how good your LLM is.

Step 5: Add Core Answer Quality Metrics

Now score the actual answers. Keep it simple:

Start here. These will give you 80% of what you need.

Step 6: Use LLM-as-a-Judge to Automate Scoring

You don’t need a QA team or whole team oversight

Feed GPT or Claude:

• The user query

• The context (retrieved docs)

• The answer

• Evaluation instructions (e.g. “Rate faithfulness 1–5”)

It returns scores like:

{
  "Faithfulness": 4,
  "Helpfulness": 5,
  "Acceptable": "Yes"
}

Validate a few by hand.

Then scale it across your test set.

Step 7: Track Product Metrics (Optional)

Once your core quality metrics are solid, track real-world UX impact:

• Latency / TTFT – Time to First Token

• Acceptable Rate – % of good answers

• Escalation Rate – % of answers that needed human backup

• Resolution Rate – % of issues resolved fully by AI

These connect AI quality to customer outcomes.

Step 8: Save Versioned Snapshots

Every time you make changes (new prompt, model, retriever):

• Re-run your synthetic set

• Save:

  • Retrieval metrics

  • Answer quality metrics

  • Logs and traces

  • Notes on what changed

Store them in Git, Notion, or even a Google Sheet. You'll build a track record over time.

Summary

You don’t need:

• Fancy MLOps stacks and expensive tools.

• 50+ metrics

• Model switching every week

You need:

• A clear objective

• A small but sharp test set

• A way to trace and debug

• The right metrics

• User feedback

• Gradual improvements

Ship faster. Learn faster. Get your AI system back on track.

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