I'm building an LLM app and need a simple evaluation workflow

Here’s a simple, practical LLM evaluation workflow you can use for an app.

1) Define what “good” means

Start with a small set of target behaviors, for example:

• Correctness
• Helpfulness
• Groundedness / faithfulness to provided context
• Tone / style
• Safety / policy compliance
• Latency and cost

Pick 3–5 key metrics only at first.

2) Create a small evaluation set

Build a dataset of 20–100 representative examples:

• Real user prompts if possible
• Edge cases
• Failure cases you’ve already seen
• A few “golden” examples with ideal answers

For each example, store:

• Input prompt
• Any retrieved context
• Expected answer or rubric
• Metadata like task type

3) Choose an evaluation method

Use a mix of:

A. Automated checks

Good for objective things:

• Exact match / regex / JSON schema validity
• Citation presence
• Toxicity filters
• Factual consistency against known answers
• Pass/fail rule checks

B. LLM-as-judge

Good for subjective things:

• Helpfulness
• Clarity
• Tone
• Reasoning quality
• Comparing two outputs

Use a consistent rubric, e.g. score 1–5 for each criterion.

C. Human review

Use for:

• High-stakes outputs
• Ambiguous cases
• Spot-checking judge accuracy

4) Build a baseline

Run your current prompt/model/version on the eval set and record:

• Output
• Score
• Latency
• Cost

This gives you a starting point for comparison.

5) Iterate one change at a time

When you change:

• Prompt
• Model
• Retrieval
• Tools
• System instructions

Re-run the same eval set and compare against baseline.

6) Track results in a simple table

Example:

Example ID	Baseline Score	New Score	Pass/Fail	Notes
001	4	5	Pass	More concise
002	2	4	Improved	Better citation
003	5	3	Regressed	Hallucinated

7) Add regression tests

Any time you find a bad output:

• Add it to the eval set
• Mark the expected behavior
• Use it as a regression test going forward

8) Set release gates

Before shipping, require:

• No critical safety failures
• No regressions on key examples
• Average score above threshold
• Latency within budget

9) Monitor in production

Even after launch:

• Log prompts/responses
• Sample outputs for review
• Track user feedback
• Watch for drift after model/provider changes

---

Simple starter workflow

If you want the absolute simplest version:

1. Collect 25 representative prompts
2. Write a 1–5 rubric for helpfulness and correctness
3. Run baseline outputs
4. Have an LLM judge compare outputs against rubric
5. Inspect failures manually
6. Add failures back into the test set
7. Re-run after every prompt/model change

---

Suggested stack

A lightweight setup might be:

• Dataset: CSV / JSONL
• Runner: Python script
• Metrics: basic scoring + pass/fail checks
• Judge: another LLM with a fixed rubric
• Tracking: simple spreadsheet at first, then MLflow/W&B if needed

---

If you want, I can also give you:

1. a minimal Python eval script,
2. a rubric template, or
3. a workflow for RAG / tool-using apps specifically.